Alteracions epigenètiques en càncer colorectal:
canvis globals i identificació de noves dianes
Memòria presentada per Jairo Rodríguez Lumbiarres
per optar al grau de Doctor en Biologia
Tesi realitzada sota la direcció del Dr. Miquel Àngel Peinado a l‟IDIBELL-Institut de Recerca Oncològica.
Tesi adscrita al Departament de Genètica de la Facultat de Biologia Universitat de Barcelona. Programa de Genètica, bienni 2003-2005.
Director Tutor
Miquel À. Peinado Ricard Albalat Jairo Rodríguez
Barcelona, Novembre de 2007
OBJECTIUS
Objectius 51
Objectius
1. Determinar la relació entre canvis globals de la metilació genòmica amb alteracions genètiques en tumors primaris humans.
2. Identificació de noves regions hipometilades en tumors colorectals amb una especial atenció sobre aquelles que continguin elements repetitius Alu. En aquest objectiu s‟hi inclou tant el mapatge com la quantificació d‟elements Alu desmetilats en cèl·lules normals i tumorals.
3. Identificació de noves regions hipermetilades en tumors colorectals i caracterització dels canvis epigenètics associats al silenciament.
RESULTATS
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CAPÍTOL 1
Chromosomal instability correlates with genome-wide DNA demethylation in human primary colorectal cancers
Jairo Rodríguez, Jordi Frigola, Elisenda Vendrell, Rosa-Ana Risques, Mario F. Fraga, Cristina Morales, Víctor Moreno, Manel Esteller, Gabriel Capellà, Miguel A. Peinado.
Cancer Research, 2006, Vol.66 (17): 8462-8468.
Els dos primers autors han contribuït per igual en aquest treball.
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Chromosomal instability correlates with genome-wide DNA demethylation in human primary colorectal cancers
Aquest treball descriptiu es centra bàsicament en l‟anàlisi de la relació existent entre alteracions genètiques i epigenètiques en tumors de CCR humans. Si bé les evidències assenyalaven que podia existir una relació causal entre hipometilació i inestabilitat genètica en diferents models, aquesta relació no s‟havia pogut veure de forma clara en tumors primaris humans. Un únic treball previ publicat l‟any 2005 indica que existeix en tumors primaris humans de CCR una correlació positiva entre nivells d‟hipometilació mesurats en elements repetitius LINE i pèrdua d‟heterozigositat en determinats loci. Tot i aquests resultats, limitacions tècniques en la mesura del grau de hipometilació (els autors mesuren el nivell d‟hipometilació amb una única determinació per PCR) així com la determinació d‟alteracions cromosòmiques en un nombre reduït de loci limiten el impacte dels resultats obtinguts.
En aquest treball, es presenten de forma conjunta els resultats obtinguts per Jordi Frigola sobre una sèrie de 83 tumors colorectals i per mi mateix sobre una segona sèrie de 50 tumors colorectals. En la primera sèrie, l‟anàlisi del grau de metilació de les mostres es realitza per AIMS. Sobre la mateixa sèrie de mostres es va calcular dany genètic per mitjà de AP-PCR. Aquesta tècnica de fingerprinting de DNA permet avaluar de forma ràpida i senzilla alteracions genètiques al llarg de tot el genoma amb la particularitat que no podem distingir entre mutacions puntuals, pèrdues o guanys de material genètic (fragments o cromosomes sencers) i reorganitzacions cromosòmiques. Els resultats obtinguts en aquesta primera sèrie indiquen que existeix una correlació positiva entre els nivells d‟hipometilació de les mostres i el nombre d‟alteracions genètiques observades per AP-PCR.
En la segona sèrie, si bé l‟anàlisi del grau d‟hipometilació dels tumors va ser idèntic per mitjà de AIMS, la quantificació d‟alteracions genètiques va ser duta a terme per mitjà de la tècnica de Comparative Genomic Hibridization o CGH. Aquesta tècnica permet veure alteracions cromosòmiques relativament grans de tipus numèric, com poden ser els guanys i pèrdues de fragments cromosòmics o cromosomes sencers, així com delecions o amplificacions de fragments cromosòmics relativament grans. En aquest cas però, no podem veure alteracions estructurals balancejades, es a dir, aquelles que no impliquen guanys o pèrdues netes de material genètic. En aquest cas també vàrem observar una correlació positiva entre el grau de hipometilació en tumor i el nombre d‟alteracions cromosòmiques, si bé la correlació era molt més bona que la observada en la primera sèrie de mostres. Aquest fet és indicatiu que el tipus d‟inestabilitat associada a desmetilació és bàsicament de tipus cromosòmic, donat que quan mesurem les alteracions genètiques d‟origen exclusivament cromosòmic la correlació observada amb el grau d‟hipometilació és significativament millor que la obtinguda entre hipometilació i alteracions genètiques de divers origen mesurades per AP-PCR. La disposició de dades clínico- patològiques de la sèrie de 50 tumors colorectals va permetre establir que la correlació entre hipometilació i alteracions cromosòmiques és independent de mutacions en altres gens importants per al manteniment de la integritat del genoma com p53, fet que indica, però no demostra, que la hipometilació pot ser causa del dany cromosòmic en tumors primaris humans.
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Supplemental Material
PATIENTS
Two series of patients preoperatively diagnosed of colorectal cancer and operated upon with curative or palliative intention were used in this study. HSP series consisted of 83 patients from the Hospital de Sant Pau (Barcelona, Spain). HUB series consisted of 50 patients from the Ciutat Sanitària i Universitària de Bellvitge (L‟Hospitalet, Barcelona, Spain). Patients were prospectively included in a study specifically designed to evaluate the prognostic value of genetic alterations in colorectal cancer and to evaluate the relationships between diet and molecular alterations. No chemo- or radiotherapy was given prior to surgery in these patients. Inclusion in the study did not influence the adjuvant treatment given. The study protocol was approved by the Ethics Committee.
Inclusion criteria in both series were: (a) electively resected primary adenocarcinomas; (b) the obtention of fresh paired normal mucosa-tumor samples within two hours after tumor removal; (c) availability of high quality DNA from paired normal and tumor tissue; and (d) no postoperative death. Additionally, HUB series only included Dukes B and C stage tumors and series HSP only included tumors negative for microsatellite instability (83 from an original collection of 93 cases) to avoid the detection of microsatellite related instability in the determination of genomic damage by Arbitrarily Primed PCR (AP-PCR) fingerprints. In series HUB it was not necessary to exclude tumors with microsatellite instabity (n=3) because it is not detected by Comparative Genomic Hybridization.
Cases were pathologically staged using Astler-Coller modification of Dukes' classification system. Surgical specimens were collected at the operating room and immediately taken to the Pathology Department in ice. Carcinomas and paired normal samples were snap frozen within two hours after removal and then stored at -80ºC.
Resultats 65
features clinicopathological by classified carcinomas 1 Table Supplemental Localization stage Dukes’ Sex Age All tumors
Female Right > 67 ≤ 67 Male Left C A - -
D B
63 20 39 44 46 37 47 36 83 n
p=0.102 p=0.494 p=0.814 p=0.744 0.181 ±0.181 ±0.145 ±0.165 ±0.178 ±0.168 ±0.175 ±0.175 ±0.169 ±0.172 0.086 0.076 0.081 0.088 0.077 0.092 0.086 0.084 0.085 GDF
. Indices of Genomic Da Genomic of . Indices
Series HSP Series
Hypomethylation Hypomethylation 0.152 ±0.152 0.089 ±0.139 0.056 ±0.139 0.085 ±0.157 0.079 ±0.168 0.077 ±0.125 0.083 ±0.138 0.080 ±0.162 0.084 ±0.149 0.082 p=0.016 p=0.554 p=0.336 p=0.200 Index
mage and DNA hypomethylation in colorectal in colorectal hypomethylation and DNA mage
a
.
35 15 21 29 26 24 22 28 50 n
Alterations 7.5 ±7.5 5.5 ±6.5 4.6 ±7.4 4.3 ±6.4 5.2 ±7.6 5.0 ±6.0 4.5 ±8.1 4.3 ±5.8 5.0 ±6.8 p=0.512 p=0.506 p=0.241 p=0.455 No of No
4.8 Series HUB Series
Hypomethylation Hypomethylation 0.120 ±0.120 0.064 ±0.125 0.057 ±0.122 0.063 ±0.122 0.062 ±0.122 0.061 ±0.122 0.064 ±0.129 0.060 ±0.116 0.063 ±0.122 0.062 p=0.788 p=0.999 p=0.978 p=0.093 Index
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Supplemental Table 2. Correlation between genomic damage and the hypomethylation index in colorectal cancers classified according to clinicopathological features
Series HSP Series HUB Variable n Correlationa p n Correlationa p All tumors 83 0.250 0.022 50 0.514 <0.001
Age ≤ 67 36 0.519 0.001 23 0.530 0.009 > 67 47 0.057 0.715 27 0.538 0.004 Sex Female 37 0.241 0.151 24 0.342 0.102 Male 46 0.293 0.048 26 0.681 <0.001 Dukes’ stage A-B 44 0.185 0.228 29 0.443 0.016 C-D 39 0.313 0.052 21 0.647 0.002 Localization Right 20 0.399 0.081 15 0.288 0.297 Left 63 0.218 0.086 35 0.618 <0.001 p53 status wild type 46 0.263 0.077 17 0.644 0.005 mutated 33 0.177 0.323 33 0.425 0.014 a Pearson‟s correlation.
Supplemental Table 3. Correlationa among the Hypomethylation index and the number of different types of chromosmal alterations
Losses Gains Numerical Estructural Hypomethylation index 0.530 0.427 0.3610 0.510 <0.001 0.002 0.010 <0.001 Losses 0.748 0.743 0.845
<0.001 <0.001 <0.001 Gains 0.886 0.726
<0.001 <0.001 Numerical 0.489
<0.001 a Pearson‟s coefficient (p value is shown bold) (n=50).
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CAPÍTOL 2
Genome-wide tracking of unmethylated DNA Alu repeats in normal and cancer cells
Jairo Rodríguez, Laura Vives, Mireia Jordà, Cristina Morales, Mar Muñoz, Elisenda Vendrell, Miguel A. Peinado
Article enviat a la revista Nucleic Acids Research.
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Genome-wide tracking of unmethylated DNA Alu repeats in normal and cancer cells
Durant dècades els elements repetitius de DNA, considerats els paràsits més senzills, han fascinat als investigadors, els quals han intentat desxifrar quin pot ser el paper d‟aquests elements en altres temps considerats DNA “escombraries” (junk DNA). Tot i que la seqüenciació del genoma humà ens ha proporcionat dades molt precises del seu nombre i distribució, desconeixem en gran mesura l‟aportació d‟aquestes seqüències a la regulació epigenètica en un context fisiològic normal i més important, com la seva desregulació epigenètica contribueix a la desestabilització del genoma i l‟aparició de malalties com el càncer. Basant-nos en mètodes preexistents, hem desenvolupat una nova metodologia que permet tant la quantificació com la identificació i el mapatge d‟elements Alu desmetilats en el genoma humà.
L‟aplicació d‟aquesta metodologia a una sèrie de tumors colorectals i les seves parelles de teixit normal ens ha permès obtenir dades del nombre d‟Alu‟s desmetilades en teixit normal i tumoral així com la identificació d‟un gran nombre de regions recurrentment hipermetilades i hipometilades en tumors colorectals. Les dades obtingudes indiquen que el nombre d‟elements Alu que de forma normal tenen la seva diana SmaI desmetilada està al voltant dels 25000 elements, mentre que aquesta figura arriba a quasi doblar-se en teixit tumoral, indicant que la hipometilació global que observem en tumors té un fort impacte sobre l‟estat de metilació dels elements Alu, com prèviament estimes globals havien indicat. El sol fet que hi hagi un nombre tant elevat d‟elements Alu desmetilats és un resultat sorprenent, donat que aquests sempre han estat considerats com els elements repetits dispersos més fortament metilats del genoma. L‟obtenció de patrons reproduïbles ens indica que hi ha en la cèl·lula normal un nombre elevat d‟elements repetitius l‟estat desmetilat dels quals està consistentment mantingut, fet que reforça la seva singularitat dins del conjunt d‟elements repetitius del genoma.
L‟estudi en profunditat d‟una de les regions hipometilades, la qual conté dos elements repetitius, indica que els canvis de la metilació que afecten a aquests elements durant el procés tumoral estan associats a canvis en les histones que podrien, al seu torn, afectar l‟estabilitat de la regió hipometilada. Aquestes dades són consistents amb la hipòtesi que els elements repetitius desmetilats proporcionen un mecanisme inductor d‟inestabilitat genètica a través de canvis en l‟estructura de la cromatina.
El gran nombre d‟alteracions de la metilació detectats per mitjà d‟aquest mètode suggereix una gran plasticitat dels patrons de metilació d‟elements Alu durant el procés tumoral, fet que requerirà investigacions futures dirigides a aclarir el paper d‟aquests i altres elements repetitius en l‟alteració dels patrons de metilació en la cèl·lula tumoral, la inducció d‟inestabilitat genètica i el silenciament transcripcional.
Resultats 69
Genome-wide tracking of unmethylated DNA Alu repeats in normal and cancer cells
Jairo Rodriguez1, Laura Vives2,3, Mireia Jordà1, Cristina Morales1, Mar Muñoz2,3, Elisenda Vendrell1 and Miguel A. Peinado1,2
1 Institut d‟Investigació Biomèdica de Bellvitge (IDIBELL), L‟Hospitalet, Catalonia, Spain 2 Institut de Medicina Predictiva i Personalitzada del Càncer (IMPPC), Badalona, Catalonia, Spain 3 Institut Català d‟Oncologia (ICO), L‟Hospitalet, Catalonia, Spain
Running title: Tracking of unmethylated Alu Keywords: interspersed repeats, DNA methylation, colorectal cancer
Correspondence: Miguel A. Peinado Institut de Medicina Predictiva i Personalitzada del Càncer (IMPPC) Cr. Can Ruti, Camí de les Escoles s/n 08916 Badalona, Catalonia, Spain email: [email protected] Tel 34-934978693 Fax 34-934978697
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ABSTRACT
Methylation of the cytosine is the most frequent epigenetic modification of DNA in mammalian cells. In humans, most of the methylated cytosines are found in CpG-rich sequences within tandem and interspersed repeats that make up to 45% of the human genome, being Alu repeats the most common family. Demethylation of Alu elements occurs in aging and cancer processes and has been associated with gene reactivation and genomic instability. By targeting the unmethylated SmaI site within the Alu sequence as a surrogate marker we have quantified and identified unmethylated Alu elements on the genomic scale. Normal colon epithelial cells contain in average 25486±10157 unmethylated Alu‟s per haploid genome, while in tumor cells this figure is 41995±17187 (p=0.004). There is an inverse relationship in Alu families with respect to their age and methylation status: the youngest elements exhibit the highest prevalence of the SmaI site (AluY: 42%; AluS: 18%, AluJ: 5%) but the lower rates of unmethylation (AluY: 1.65%; AluS: 3.1%, AluJ: 12%). Data are consistent with a stronger silencing pressure on the youngest repetitive elements, which are closer to genes. Further insights into the functional implications of atypical unmethylation states in Alu elements will surely contribute to decipher genomic organization and gene regulation in complex organisms.
Funding This work was supported by a grant from the Ministry of Education and Science (SAF2006/351) and the Consolider-Ingenio 2010 Program (CSD2006-49).
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INTRODUCTION
Progress in large-scale sequencing projects is critical to identify and decipher gene organization and regulation in many species including human. Nevertheless, cumulated evidences indicate that the complexity of living organisms is not just a direct outcome of the number of coding sequences and that the presence of multiple regulatory mechanisms accounts for a significant part of biological complexity (1,2). Among these mechanisms, repetitive elements may play a key role in gene regulation and genomic structure. Active transposable elements are involved in genome rearrangement and illegitimate recombination and can also influence gene expression by altering splicing or by acting as enhancers or promoters (3-7). Advances in the understanding of epigenetic mechanisms that regulate this repetitive elements may contribute to elucidate their specific participation in biological processes (8).
Silenced regions in mammals and other vertebrates are differentiated, although not exclusively, by the presence of DNA methylation (reviewed in (9)). Methylation of the cytosine is an epigenetic modification of DNA that plays an important role in the control of gene expression and chromosome structure in mammalian cells (reviewed in (10-13)). Most of the 5- methylCytosines are found in CpG-rich sequences within tandem and interspersed repeats (9,12) which previous estimates indicate that constitute up to 45% of the human genome (14). Among these repeats, Alu‟s, with more than 1 million copies per haploid genome, are considered the most successful family (15). Interestingly, Alu‟s are not randomly distributed within the human genome as they tend to accumulate in gene rich regions (14,16,17). Previous works have estimated that Alu elements harbor up to 33% of the total number of CpG sites in the genome (18) and have been reported to be highly methylated in most somatic tissues (18- 20). Methylation represents the primary mechanism of transposon suppression and active transposons are demethylated in mammalian genomes (12). It has been proposed that regions of the genome containing repetitive elements might be masked by compartmentalization of the chromatin, resulting in a reduction of the effective size of the genome (21).
Noteworthy, even though a vast number of CpG dinucleotides are provided by the collection of repetitive sequences in the human genome, this dinucleotide is greatly under-represented throughout the genome, but it can be found at close to its expected frequency in small genomic regions (200bp to a few kb), known as CpG islands (22). These areas are “protected” from methylation and are located in the proximal promoter regions of 75% of human genes (12,13,22). Methylated CpG islands are strongly and hereditably repressed (12). Hence DNA methylation is usually considered as a sign of long-term inactivation (9,10,12).
Cancer cells are characterized by the accumulation of both genetic and epigenetic changes. Widespread genomic hypomethylation is an early alteration in carcinogenesis and has been associated with genomic disruption and genetic instability (23-27). Repeats unmasked by demethylation are likely to facilitate rearrangements due to mitotic recombination and unwanted transcription (28-30). Alternatively, aberrant de novo methylation of CpG islands is a hallmark of human cancers and is associated with epigenetic silencing of multiple tumor suppressor genes (31-37). Therefore, the screening for differentially methylated sequences in
Resultats 72 tumors appears as a key tool to further understand the molecular mechanisms underlying malignant transformation of cells. Although, the repertoire of methylation screening methodologies has expanded widely (37-39), and different approaches have been used to make bulk estimates of methylation in repetitive elements (40,41), there is still a lack of screening strategies that specifically allow a feasible identification of DNA methylation alterations in repetitive elements (21).
Here we report two variants of a novel methodology to quantify and identify unmethylated Alu sequences. The CpG site within the consensus Alu sequence AACCCGGG is used as a surrogate reporter of methylation. Unmethylated sites are cut with the methylation-sensitive restriction endonuclease SmaI (CCCGGG) and an adaptor is ligated to the DNA ends. Quantification of UnMethylated Alus (QUMA) is performed by real-time amplification of the digested and adaptor- ligated DNA using an Alu consensus primer that anneals upstream of the SmaI site and an adaptor primer extended with the TT dinucleotide in its 3‟ end (Figure 1A). The product generated by this approach is completely inside the Alu element and hence it is not possible to make a unique identification. As an alternative approach, we have also performed restrained amplification of digested and adaptor-ligated DNA fragments that are flanked by two close SmaI sites. In this case, the same primer homologous to the adaptor with the additional TT nucleotides at the 3‟ end to enrich for Alu sequences is used in absence of the Alu consensus primer (Figure 1B). This second approach is named Amplification of UnMethylated Alu‟s (AUMA) and results in a complex representation of unique DNA sequences flanked by two unmethylated SmaI sites. When resolved by high resolution electrophoresis, the AUMA generated sequences appear as a fingerprint characteristic of each sample (Figure 2) and individual scoring and identification of each band can be performed. Because AUMA‟s stringency is based on a short sequence (AACCCGGG) that is found preferentially but not exclusively in Alu elements, other unmethylated sequences are also present in AUMA fingerprints.
Application of QUMA and AUMA to a series of colorectal carcinomas and their paired normal mucosa has offered global estimates of unmethylation of Alu elements in normal and cancer cells and has revealed a large collection of unique sequences that undergo highly recurrent hypomethylation and hypermethylation in colorectal tumors.
MATERIALS AND METHODS
Tissues and cell lines
Fifty colorectal carcinomas and their paired non-adjacent areas of normal colonic mucosa were included in this analysis. Samples were collected simultaneously as fresh specimens and snap- frozen within 2 h of removal and then stored at -80ºC. All samples were obtained from the Ciutat Sanitària i Universitària de Bellvitge (Barcelona, Spain). The study protocol was approved by the Ethics Committee. Human colon cancer cell lines (HT29, SW480, HCT116, LoVo, DLD-1, CaCo-2 and LS174T) were obtained from the American Type Culture Collection (ATCC; Manassas, VA). KM12C and KM12SM cells were generously provided by A. Fabra. DNA from
Resultats 73 tumor-normal pairs was obtained by conventional organic extraction and ethanol precipitation. DNA purity and quality was checked in a 0.8% agarose gel electrophoresis. RNA from cell lines was obtained by phenol-chloroform extraction and ethanol precipitation, following standard procedures.
Bioinformatic analysis
The distribution of SmaI sites, putative amplification hits, PCR homologies, CpG islands and repetitive elements was assessed using the human genome assembly 36.1 from NCBI. Data were obtained from the Repbase (http://www.girinst.org/repbase/index.html) and the Genome Browser Databases (http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/). Only assembled chromosome fragments were considered. A Perl routine was used to score all positions containing the target sequences in all chromosomes (available from the authors upon request). Data were analyzed using Excel spreadsheets.
To calculate the proportion of unmethylated Alu elements at the genomic level, the number of AUMA hits identified in bioinformatic analysis was corrected according to the distribution of experimentally generated AUMA products performing Monte Carlo simulations. One thousand Monte Carlo simulations were performed using an Excel Add-in (available at www.wabash.edu/econometrics). In Monte Carlo simulations it was assumed that 80-100% of SmaI sites at CpG islands are unmethylated and that 50-100% of SmaI sites in other genomic regions different from Alu‟s and CpG islands are unmethylated.
Quantification of UnMethylated Alu (QUMA)
One microgram of DNA was digested with 20 U of the methylation sensitive restriction endonuclease SmaI (Roche Diagnostics GmbH, Mannheim, Germany) for 16h at 30ºC, leaving cleaved fragments with blunt ends (CCC/GGG). Adaptors were prepared incubating the oligonucleotides Blue (CCGAATTCGCAAAGCTCTGA) and the 5‟ phosphorilated MCF oligonucleotide (TCAGAGCTTTGCGAAT) at 65ºC for 2 min, and then cooling to room temperature for 30-60 min. One microgram of the digested DNA was ligated to 2nmol of adaptor using T4 DNA ligase (New England Biolabs, Beverly, MA). Subsequent digestion of the ligated products with the methylation insensitive restriction endonuclease XmaI (New England Biolabs) was performed to avoid amplifications from non-digested methylated Alu‟s. The products were purified using the GFX Kit (Amersham Biosciences, Buckinghamshire, UK) and eluted in 250 μl of sterile water.
Quantitative real time PCR was performed using 1ng (the equivalent of 333 genomes) of DNA in a LightCycler 480 real time PCR system with Fast Start Master SYBR Green I kit (Roche). For PCR conditions see Supplemental data. The downstream BAu-TT primer (constituted by the 3‟ end of Blue primer, and the GGGTT sequence including the GGG 3‟ side of the cut SmaI site and the Alu homologous TT dinucleotide, ATTCGCAAAGCTCTGAGGGTT) and the upstream primer was an Alu consensus sequence (CCGTCTCTACTAAAAATACA) (see Supplemental data). Magnitudes were expressed as number of unmethylated Alu‟s per haploid genome after DNA
Resultats 74 input normalization. The number of haploid genomes present in the test tube was determined in the same multiwell plate by quantification of Alu sequences irrespectively of the methylation state. A real time PCR using Alu consensus primers upstream of the CCCGGG site was performed (see Supplemental data) and the number of genomes was calculated against a standard curve constructed with a reference genomic DNA measured by UV spectrophotometry.
To determine the efficiency of the assay and to perform absolute quantification, an external Alu product generated by PCR from a DNA fragment containing an AluSx element was used as standard (Supplemental methods). The number of copies of the external control was spectrophotometrically quantified and dilution curves were generated and treated as samples. Comparison of dilution curves before and after sample processing indicated that the mean recovery was 73%. DNA samples overdigested with the the methylation insensitive XmaI endonuclease were spiked with different amounts of the external standard and processed. The sensitivity of the QUMA detection was 100 unmethylated Alu‟s per haploid genome (Supplemental figure 1) using 1 ng of genomic DNA per PCR. A linear response was observed between 1000 and 100000 unmethylated Alu‟s per haploid genome (Supplemental Data).
Amplification of UnMethylated Alu (AUMA)
DNA digestion with SmaI enzyme and ligation to the linker was performed as described above for QUMA, except for the XmaI digestion which was skipped. The product was purified using the GFX Kit (Amersham Biosciences) and eluted in 250 μl of sterile water. Six different chimeric primers constituted by the 3‟ end of the Blue primer sequence (ATTCGCAAAGCTCTGA), the cut SmaI site (GGG) and two, four or seven additional nucleotides homologous to the Alu consensus sequence were used to enrich for Alu sequences (see supplemental Methods). Three primers were designed to amplify „upstream‟ of the SmaI site (towards Alu promoter): BAu-TT, BAu-TTCA, BAu-TTCAAGC. Three other primers were designed to amplify „downstream‟ (towards Alu poly-A): BAd-AG, BAd-AGGC, BAd-AGGCGGA. Letters after the dash correspond to the 3‟ sequence of the primer (see supplemental data). Data reported here were obtained by using the BAu-TT primer.
In each PCR reaction only one primer was used at a time. Products were resolved on denaturing sequencing gels. Although bands can be visualized by silver staining of the gels, radioactive AUMA‟s were performed for normal-tumor comparisons. A more detailed description of the PCR and the visualization of the bands is given as Supplemental data.
Only sharp bands that were reproducible and clearly distinguishable from the background were tagged and included in the analysis. Faint bands with inconsistent display due to small variations in gel electrophoresis resolution were not considered. Band reproducibility was assessed with the analysis of PCR duplicates of three independent sample digests from two different samples and PCR replicates from the same digest from four paired tumor-normal samples. AUMA fingerprints were visually checked for methylation differences between bands in the tumor with regard to its paired normal mucosa. Under these premises a given band was scored according to three possible behaviors: hypomethylation (increased intensity in the
Resultats 75 tumor), hypermethylation (decreased intensity in the tumor) and no change (no substantial difference in intensity between normal and tumor samples) (Figure 2). Only those bands showing clear changes in their intensities in the fingerprint were considered to represent methylation changes. This is consistent with previous studies done using a related technique (42,43).
Competitive hybridization of AUMA products to metaphase chromosomes and BAC arrays
The origin and chromosomal distribution of sequences generated by AUMA was performed using procedures analogous to CGH. Briefly, an AUMA product obtained from a normal tissue DNA was purified using Jet quick PCR product purification kit (Genomed, Löhne, Germany) and labeled with SpectrumRed dUTP (Vysis, Downers Grove, IL) using a Nick Translation kit (Vysis). Similarly, genomic DNA of the same normal sample was labeled with SpectrumGreen dUTP (Vysis) and both probes were cohybridized to metaphase chromosomes. Procedures and image analysis were performed as described (44).
Differential normal-tumor representation of AUMA at the genomic scale was performed by competitive hybridization of AUMA products to BAC arrays. AUMA products from two normal- tumor pairs were purified using Jet quick PCR product purification kit (Genomed, Löhne, Germany) and 1 µg was labeled with dCTP-Cy3 or dCTP-Cy5 (Amersham Biosciences, UK) by use of the Bioprime DNA Labeling System (Invitrogen, Carlsbad, CA). Probes were hybridized to SpectralChip 2600 BAC arrays (Spectral Genomics, Houston, TX) following the manufacturer‟s instructions. Arrays were scanned with a ScanArray 4000 (GSI Lumonics, Watertown, MA) and processed with GenePix software (Axon Instruments, Union City, CA). The resulting data were processed to filter out low-quality spots based on spot area and similarity of readings between the two replicates of each BAC. Data manipulation was performed using Excel spreadsheets. Because AUMA products are not evenly distributed along chromosomes, only BACs with intensities above the 10% of maximum intensity in at least one of the two channels were considered for ratio calculations. The pattern of chromosomal alterations in these two tumors was determined by conventional CGH as described (44).
Isolation and cloning of AUMA tagged bands
DNA excised from gels was directly amplified with the same primer used in AUMA (BAu-TT) (Supplemental figure 2). The amplified product was cloned into plasmid vectors using the pGEM-T easy vector System I cloning kit (Promega, Madison, WI). Automated sequencing of multiple colonies was performed using the Big Dye Terminator v3.1 Cycle Sequencing kit (Applied Biosystems, Foster City, CA) to ascertain the unique identity of the isolated band. Sequence homologies were searched for using the Blat engine (http://genome.ucsc.edu/). Selected clones corresponding to AUMA isolated bands were radioactively labeled and used as a probe to confirm the identity of the excised band by hybridization to AUMA fingerprints as previously described (45).
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Bisulfite genomic sequencing
Differential methylation observed in some AUMA tagged bands was confirmed by direct sequencing of bisulfite treated normal and tumor DNA as previously described (46). Prior to sequencing, DNA was amplified using a nested or semi-nested PCR approach, as appropriate. Three independent PCRs were done and products were pooled to ensure a representative sequencing. The sequence of PCR primers is described in Supplemental Data.
Histone modification analysis by Chromatin ImmunoPrecipitation (ChIP)
Briefly, 6x106 cells were washed twice with PBS and cross-linked on the culture plate for 15 min at room temperature in the presence of 0,5% formaldehyde. Cross linking reaction was stopped by adding 0.125M glycine. All subsequent steps were carried out at 4˚C. All buffers were pre- chilled and contained protease inhibitors (Complete Mini, Roche). Cells were washed twice with PBS and then scraped. Collected pellets were dissolved in 1ml lysis buffer (1% SDS, 5mM EDTA, 50mM Tris pH8) and were sonicated in a cold ethanol bath for 10 cycles at 100% amplitude using a UP50H sonicator (Hielscher, Teltow, Germany). Chromatin fragmentation was visualized in 1% agarose gel. Obtained fragments were in the 200 to 500 bp range. Soluble chromatin was obtained by centrifuging the sonicated samples at 14.000g for 10 min at 4˚C. The soluble fraction was diluted 1/10 in dilution buffer (1% Triton X-100, 2mM EDTA, 20mM Tris pH8, 150mM NaCl) then aliquoted and stored at -80˚C until use.
Immunoprecipitation was carried out at 4˚C by adding 5 to 10 µg of the desired antibody to 1ml of chromatin. Chromatin-antibody complexes were immunoprecipitated with specific antibodies using a protein A/G 50% slurry (Upstate, Millipore, Billerica, MA) and subsequently washed and eluted according to the manufacturer‟s instructions. Antibodies against acetylated H3 K9/K14 (Upstate), dimethylated H3 K79 and trimethylated H3 K9 (Abcam, Cambridge, UK) were used. Enrichment for a given chromatin modification was quantified as a fold enrichment over the input using quantitative real time PCR (Roche). For every PCR, a standard curve was obtained to assess amplification efficiency. All quantifications were performed in duplicate.
RESULTS
Genomic estimation of the targets and evaluation of the adequacy of the approach by computational analysis.
The availability of the human genome map has allowed us to make a detailed estimation of the frequency and distribution of the sites targeted by our approaches on the genomic scale. A Perl routine was used to score all positions containing the target sequences in all chromosomes and was also applied to perform a virtual AUMA (see Material and Methods). Some of the most important data derived from the bioinformatic analysis are shown in table 1 and figure 3.
Resultats 77
Because of the C to T mutational bias at CpG sites (47), any amplification method relying on the consensus sequence (see supplemental data) will only cover a fraction of all the Alu‟s. Therefore it is important to estimate the degree of representativity of the methods used here if genome-wide estimations are to be made. Alu repeats constitute 7.4% of the human genome but accumulate 40.7% of all SmaI sites (Table 1). Nearly 200000 Alu‟s (18% of all Alu‟s) contain a SmaI site and 155000 retain the AACCCGGG consensus sequence (Table 1 and Figure 3) and are therefore potential targets of QUMA and AUMA. While 38.0% of the youngest AluY elements contain this sequence, the proportions drop to 14.8% and 0.4% in AluS and AluJ families, respectively (Table 1 and Figure 3). These frequencies are consistent with a higher C to T transition trend at CpG sites in older Alu‟s (47).
The representativity of AUMA was analyzed by a virtual bioinformatic assay of the human genome sequence. A total of 168309 AACCCGGG (or CCCGGGTT) hits were identified throughout the genome, with 92.9% of all hits within Alu elements (Table 1 and Figure 3). This implies that 14.2% of all Alu elements contained the AACCCGGG sequence. Another 1.0% of the hits were in CpG islands and 6.8% in the rest of the genome (including unique sequences and other repeats) (Table 1 and Figure 3). As expected, Alu elements containing the target sequence mostly belonged to the AluS (2/3) and AluY families (1/3), with a minimal representation of the older family AluJ (less than 1%). Virtual AUMA determined the presence of 5498 putative products of less than 1Kb (the sequence AACCCGGG in the up strand and the sequence CCCGGGTT in the down strand at a distance of less than 1 kb). Although actual AUMA PCR products may reach 2 kb length (see below), we used the 1 kb limit to compare with AUMA isolated bands, which were shorter than 1 kb. Most virtual AUMA products contained an Alu element at at least one of the ends (93%).
Quantification of Unmethylated Alu in normal and tumor tissues.
The QUMA approach was applied to quantify unmethylated Alu‟s in a series of 18 colorectal carcinomas and their paired normal colonic mucosa. An external DNA fragment containing an AluSx element was used as a standard (see Material and Methods and Supplemental Data) in order to make an absolute quantification of the number of unmethylated Alu‟s. Replicates and dilution curves of the samples and standard were performed to assess reproducibility, sensitivity and accuracy (Supplemental Figure 1). Results were normalized by assessment of the number of haploid genomes per test tube (See Material and Methods). The average number of unmethylated Alu‟s per haploid genome was 25486±10157 in normal mucosa, and 41995±17187 in tumor samples (p=0.004, paired t-test) (Figure 4). At bioinformatic level we identified a total of 168309 Alu elements containing the AACCCGGG sequence (potential targets of QUMA) (Table 1). Therefore we estimate that 15.1% of Alu repeats with a AACCCGGG site are unmethylated in the average normal colonic mucosa cell, while this figure is 24.9% in the cancer cell. Considering that the human genome contains approximately 1.1 million Alu elements, these estimates indicate that unmethylated Alu‟s constitute the 2.3% and 3.8% of all Alu‟s in the normal and tumor tissues respectively.
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Set-up and optimization of AUMA fingerprinting
Because QUMA products are fully contained within the Alu sequence, it is not possible to identify and position in the genome the unmethylated Alu elements. To achieve this it is necessary to amplify the targeted unmethylated Alu element together with an adjacent unique sequence. This was attained through the use of the second method, the Amplification of UnMethylated Alus (AUMA). The AUMA also targets the unmethylated AACCCGGG sequence, as in QUMA, but in this case a single primer is used in the PCR (BAu-TT) (see Figure 1 and Supplemental Data). Moreover, the product is resolved on a high resolution gel electrophoresis resulting in a band-rich fingerprint. AUMA bands correspond to sequences flanked by two unmethylated target sequences in opposite strands and sufficiently close to allow PCR amplification (Figure 1 and Supplemental Data). Since 92% of the AACCCGGG occurences in the human genome are in Alu‟s (Table 1), the approach is largely biased towards the amplification of unmethylated Alu‟s. The presence of non Alu sequences at one of the ends or between two repetitive elements allows the positioning within the genome map of all products.
AUMA products generated using the BAu-TT primer produced highly reproducible fingerprints consisting of bands ranging from ~100 to ~2000 bp when resolved in high resolution sequencing gels (Figure 2 and Supplemental Figure 2). Some well identifiable bands (up to 5 per experiment) showed random display in both intra-assay and inter-assay replicates (Supplemental figure 2) and were not considered for analysis. A subset of 110 bands with consistent display among all the experiments were tagged and selected for comparative analysis between samples (see Materials and Methods).
It should be noted that different fingerprints containing alternative representations may be obtained by AUMA just by using primers that either amplify from the SmaI site towards the Alu promoter (upstream Alu amplification) or towards the Alu poly-A tail (downstream Alu amplification). Also the stringency of the Alu selection may be increased by using longer primers containing additional nucleotides corresponding to the Alu consensus sequence (See Material and Methods). An illustrative example of AUMA fingerprints generated with different Alu- upstream and Alu-downstream primers is shown in supplemental figure 2. All the data reported in this paper regarding AUMA were obtained using the BAu-TT primer.
Chromosomal origin of AUMA products
Competitive hybridization between AUMA products and genomic DNA on metaphase chromosomes yielded a characteristic hybridization pattern demonstrating the unequal distribution of AUMA products along the human genome (Figure 5A). Competitive hybridization of AUMA products to BAC arrays showed profiles consistent with those obtained on metaphase chromosomes (Figure 5B). The highest AUMA signal was detected in whole chromosomes 16, 17 and 19 in contrast with chromosomes 2, 13, 18 and X which were mainly labelled by genomic DNA. Other chromosomes showed a discrete pattern of AUMA product hybridization, in which telomeric bands in chromosomes 1, 4, 5, 9, 12 and X, and interstitial bands in chromosomes 1, 3, 7, 11 and 12 are the most prominent examples.
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Identification of AUMA amplified DNA products
To determine the identity of bands displayed by AUMA, 38 tagged bands were isolated and cloned. Multiple clones from each band were sequenced, resulting in a total of 49 different sequences due to the coincidence of more than one sequence in some bands. Characterized bands included bands displaying no changes in the normal-tumor comparisons and bands recurrently altered in the tumor. Table 2 summarizes the main features of a subset of the bands showing recurrent alterations. A list of all the sequences isolated from AUMA fingerprints is provided as Supplementary Table 1. All sequenced bands contained a region of non-repetitive sequence and matched with the BLAST reference sequence, allowing the assignment of a unique chromosomal localization. The BLAST reference sequence corresponding to the 49 sequences isolated from the AUMA fingerprint presented the target sequence CCCGGGTT including the SmaI at both ends. Southern blot analysis of selected cloned sequences showing coincidental size was performed to confirm its correspondence with the band displayed in AUMA fingerprints (Supplemental figure 4).
To obtain a more representative collection of AUMA bands, 200 clones obtained from normal tissue AUMA products were sequenced. The analysis revealed 88 additional sequences. This resulted in a total of 137 different loci represented in AUMA (Supplemental table 1). Most sequences obtained by random cloning were also flanked by two AACCCGGG sequences in opposite DNA strands. Nevertheless, in 27 sequences the AACCCGGG site was only present at one of the ends, with the other end showing high homology with the primer although it was not a perfect match. The presence of these sequences suggests that, in some instances, a single cut in the sequence may be enough to produce an amplifiable fragment. This is not considered an artifact since these bands still represent an unmethylated AACCCGGG site.
Genome-wide estimations of unmethylation in Alu’s and distribution by subfamily
Of the 137 identified loci represented in AUMA, 114 were isolated from normal tissue DNA and 23 from tumor DNA. Half of the sequences contained an Alu sequence at one of the ends and two were flanked by two inverted Alu‟s. AUMA sequences isolated from tumor tissue and not present in normal tissue (this corresponds to a tumor specific hypomethylation) showed a higher proportion of Alu elements (16 out of 23, 70%), and included one band flanked by two inverted Alu‟s. Globally, 78 unmethylated Alu elements were identified and positioned in the human genome map.
To study the genomic distribution of unmethylated sequences in normal colon mucosa we only considered the 114 bands obtained from normal tissue. This resulted in a total of 201 unmethylated hits characterized throughout the genome. The nature of the sequences represented in actual AUMA showed striking differences with the distribution expected from the virtual AUMA analysis. The methylation status of the sequence is likely to be the main (if not the only) source of these differences because the virtual AUMA did not consider this state. Therefore we can use these differences to estimate the degree of unmethylation of the Alu repeats. Only 29.4% of the AUMA ends consisted of Alu‟s, as compared with the expected
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92.9% resulting from the bioinformatic analysis. The highest downrepresentation corresponded to the youngest AluY family, which was present in 6.0% of the AUMA ends, while it was expected to add up to 33.1% in virtual AUMA. AluS representation in actual and virtual AUMA was 22.3% and 60%, respectively. Interestingly, AluJ representations, in both actual and virtual AUMA, were closer (1.0% and 0.7%, respectively) (figure 3 and table 1). These results suggest that there is a stronger pressure to methylate younger Alus. Alternatively, the hits corresponding to CpG islands were overrepresented in actual versus virtual AUMA by a factor of nearly 25 fold (27.4 versus 1.1%), consistently with the unmethylated status of most CpG islands (figure 3 and table 1). The rest of the hits were located in different types of repetitive elements (MIR, MER, LTR, LINE, etc.) and unique sequences (Supplemental table 1). The miscellaneous collection of sequences (“Rest”) was over-represented by about 7 fold (observed hits: 43.3%; expected hits: 5.9%, table 1). The 46 AUMA hits represented by the 23 bands specific of tumor tissue showed a higher proportion of Alu‟s (41%, versus 29% in normal tissue) but similar distribution by Alu type (10 AluS, 5 AluY, 1 AluJ, 4 in CpG islands and 26 in other sequences).
To calculate the proportion and distribution of unmethylated Alu elements on a genomic scale, we performed Monte Carlo simulations taking into account the observed and expected distribution of hits in each Alu family and CpG islands and the rest of sequences (See Material and Methods). We estimate that at least 4104 Alu elements are unmethylated or partially unmethylated in normal colonic mucosa. This corresponds to 2.64% of all Alu elements containing the target sequence AACCCGGG (Table 1). Although AluS and AluY represent the majority of these sequences it should be noted that the methylation pressure is inverse to the conservation of the SmaI site. That is, the most conserved and younger AluY family shows the lowest relative rate of unmethylation; and the older and more degenerated AluJ family exhibits the highest unmethylation.
Application of AUMA to detect differential DNA methylation in colorectal carcinomas
In order to test the usefulness of the method for the detection of new altered methylation targets we applied AUMA to a series of 50 colorectal carcinomas and their paired normal mucosa. Two cases were excluded from the analysis due to recurring experimental failure of the normal or tumor tissue DNA. For the rest of 48 normal-tumor pairs, consistent and fully readable fingerprints were generated and evaluated for normal-tumor differential representation. A given case presented, on average, 107 ± 2.9 informative bands (range 98- 110). The variation was due to polymorphic display or variable resolution power of gel electrophoresis.
In this study, only those bands showing clear intensity differences between normal and tumor tissue fingerprints (Figure 2) have been scored as methylation changes since they are more likely to reflect tumor-wide alterations. Because the fingerprints represent sequences flanked by two unmethylated sites, a decreased intensity in a given band in the tumor in regard to the paired normal tissue is indicative of hypermethylation, while an increased intensity corresponds to hypomethylation (Figure 2). All tumors displayed changes in regard to the paired normal
Resultats 81 tissue. The average tumor showed 19 ± 7 (range 6-37) hypomethylations and 22 ± 10 (range 1-39) hypermethylations. It is of note that hypomethylations could either be seen as an increase in the intensity of a pre-existing band in the normal tissue or a de novo appearance of an non-existent band in the normal tissue. This contrasts with hypermethylation events, which rarely showed the complete loss of a band in the tumor sample, most likely due to the unavoidable contamination of normal tissue.
Virtually, all tagged bands (109 out of 110) were found to be altered in at least one tumor when compared to its normal paired mucosa. AUMA tagged bands presented a wide distribution in the hypomethylation/hypermethylation rates (proportion of tumors showing differential display compared to the paired normal tissue) (Figure 6). Hypomethylation and hypermethylation showed a strong negative correlation (r=-0.55 and P<0.0001), indicating that most bands tended to be either hypomethylated or hypermethylated. A large proportion of tagged bands (78 bands) were recurrently altered in over 25% of the cases included in this series.
In order to determine whether normal-tumor differences were limited to isolated independent loci or changes might affect larger chromosomal regions we compared the distribution of AUMA products generated from two paired normal and tumor tissues and hybridized to BAC arrays. Differential hybridization was observed in many BACs, suggesting that relatively large regions encompassing from several hundred Kbs to a few Mbs may undergo concurrent hypomethylation or hypermethylation. Telomeric regions of many chromosomes contained most of the differential display (Figure 5C). The differential methylation profiles were unaffected by chromosomal dosage as demonstrated by its independence of chromosomal losses and gains (as detected by Comparative Genomic Hybridization (CGH) (Supplemental figure 5).
Validation of methylation changes detected by AUMA
To confirm that the changes observed in AUMA fingerprints corresponded to actual changes in the methylation status of the sequence, eight different sequences obtained from AUMA fingerprints were analyzed in normal and tumor tissues by direct sequencing of sodium bisulfite treated DNA‟s (Table 2). AUMA detected hypomethylations and hypermethylations were confirmed in most cases. Moreover it was demonstrated that methylation changes affected not only the CpG in at least one of the two flanking SmaI sites (whose methylation prevents AUMA representation) but also neighboring CpG‟s (supplemental figure 6). In two samples, AUMA changes observed in the tumor could not be confirmed by bisulfite sequencing, suggesting that the change might affect only a small fraction of tumor cells and that both methods may exhibit different sensitivities. The presence of minor subpopulations can be detected using more sensitive techniques, i.e. the Methylation Specific PCR or by sequencing of multiples clones.
Functional implications of changes detected by AUMA
Next, we wondered if DNA methylation changes detected by AUMA may have any functional consequences. We chose one of the most recurrent hypomethylated AUMA sequences (Aq3)
Resultats 82 and performed an insightful epigenetic characterization of the region in a series of normal- tumor pairs and in colon cancer cell lines.
Aq3 band is recurrently hypomethylated in tumors according to AUMA fingerprints (Figure 7A). It represents a sequence situated in the eighth intron of the MYOM2 gene (Table 2) and does not fall inside or close to any CpG island. The SmaI sites are located in a MLT1A repeat and an AluYd3 element. The methylation status of the two flanking regions of the AUMA band (465 bp and 213 bp long spanning 20 and 11 CpGs respectively) was analyzed by bisulfite direct sequencing (Figure 7B). Confirmation of AUMA data was performed in three normal-tumor pairs that displayed the de novo appearance of the Aq3 band in AUMA fingerprints (cases 17, 63 and 74) and two cases lacking this band in both normal and tumor pair (cases 53 and 99) (Figure 7A), as well as five cell lines (HCT116, DLD-1, LoVo, HT29 and CaCo2). All normal tissues as well as tumors 53 and 99 showed heavy methylation of this region (Figure 7C). In contrast to this and in agreement with AUMA results, tumors 17, 63 and 74 exhibited hypomethylation at most CpGs. Cell lines showed variable profiles of DNA methylation, with CaCo2 exhibiting unmethylation of the MLT1A element but heavy methylation of the AluYd3 element, which was also heavily methylated in HCT116 cells but not in the rest of the cell lines tested. MYOM2 expression levels analyzed by real time RT-PCR were not affected by the methylation status of this sequence (data not shown). Further 45 normal-tumor pairs were analyzed for methylation of the AluYd3 element by real-time dissociation analysis (Supplemental figure 7) and it was found hypomethylated in 26 tumors (58%).
Next, we wondered whether the DNA methylation status of the AluYd3 element was associated with alternative chromatin states. We performed Chromatin ImmunoPrecipitation (ChIP) analysis of histone 3 (H3) modifications indicative of active chromatin: acetylation of lysines 9 and 14 (AcH3K9/K14), and dimethylation of lysine 79 (2mH3K79); and silent chromatin: trimethylation of lysine 9 (3mH3K9). These histone marks were compared between cell lines HCT116 and LoVo (with 100% and 30% methylation of the AluY element, respectively). The silencing mark 3mH3K9 was 3.5 fold higher in HCT116 cells compared to the LoVo cell line (Figure 7D). No differences in active marks were observed and these were significantly lower than the silencing mark 3mH3K9. When HCT116 cells were treated with the demethylating agent 5-aza-2‟-deoxycytidine (5AzaC) and the histone deacetylase inhibitor trichostatin A (TSA), a moderate decrease in the amount of the 3mH3K9 mark was observed (Figure 7E). As a whole, these data suggest that DNA methylation changes in this AluYd3 element are accompanied by altered chromatin states. The molecular consequences of such epigenetic changes remain to be identified.
DISCUSSION
Epigenetic states of Alu elements
Full genome sequencing has provided precise maps of repetitive elements, and several studies have investigated their distribution and relationship with genome structure (48-51). More
Resultats 83 recently, a few studies have explored sequence-dependent associations between repetitive elements and the epigenetic landscape. There is a characteristic distribution of interspersed elements along methylated and unmethylated domains, with most elements in the methylated compartment of the genome (21). Nevertheless, SINEs, which include Alu elements, are the repetitive sequences most commonly found in unmethyated domains (21) and some Alu elements may contain discriminatory motifs associated with methylation-resistant CpG islands (52). Somatic cells show unstable epigenetic profiles in repetitive elements as demonstrated by global measurements of either DNA methylation (18,20,40,41) or histone modifications (53,54). Recent studies have revealed interindividual variability in DNA methylation profiles at specific Alu elements (55), and Fraga and colleagues detected epigenetic changes arising during the lifetime of monozygotic twins in Alu elements and other sequences (56).
Beyond these few studies, the extension and nature of the epigenetic state of interspersed elements is largely unknown. Global estimates of DNA methylation in repetitive elements have been obtained by Southern blot analyses (reviewed in (30)) and, more recently, by using approaches based on bisulfite conversion of the unmethylated cytosine (40,41,57). These studies have confirmed the global hypomethylation of most tumors but they do not provide detailed information on the nature and localization of the unmethylated elements. In silico analysis has revealed that a number of Alu elements close to CpG islands retain a high proportion of CpG sites, and this is presumed to be a sign of unmethylation (58), but no experimental proof has been provided. In our point of view, the lack of simple, specific and sensitive methodologies to screen for epigenetic changes in repetitive elements on the genomic scale has precluded a clearer understanding of the nature and implications of these sequences in cell biology.
Properties of QUMA and AUMA
Here we report a systematic screening of unmethylated Alus as a tool to determine the extent of DNA hypomethylation, to identify specifically unmethylated elements and to detect epigenetic alterations in cancer cells. The QUMA is a very simple and specific method and provides accurate relative estimates of the number of unmethylated elements. The QUMA is specially appropriate for comparative studies, but also provides a raw quantitation of the number of unmethylated elements per haploid genome, outlining the extent of hypomethylated Alu‟s in normal and pathologic cells. QUMA analysis indicates that about 1 out of 6 Alu elements containing the AACCCGGG site are unmethylated, while in tumors, this figure nearly doubles in agreement with previous studies (reviewed in (23)). Although these analyses are likely to generate good estimates at comparative level (between samples), absolute values should be treated with caution because the determination refers to a single CpG site within the Alu element.
To date there is still a lack of proper methodologies allowing genome-wide screenings for recurrent hypomethylated regions that may have some impact on tumor biology. Even though QUMA and other methodologies (41,59) allow quantitation of unmethylated repeats, they do not provide a straight forward approach to identify and map the amplified targets. At this point,
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AUMA takes us a step further, allowing the undoubtful identification of hypomethylated sequences, in addition to hypermethylated targets. Although AUMA is specially suited to determine the nature of the unmethylated elements, it also allows the calculation of global unmethylation in Alu elements. Nevertheless, it should be taken into account that this is an indirect measure, because it relies in the extent of methylation in CpG islands and other sequences. Moreover, unmethylation of a second SmaI site near the Alu is also required to generate the AUMA band and hence to be detected.
Due to sequence degeneration, both QUMA and AUMA are more effective in screening for unmethylation in younger elements. This trend is more clearly seen in AUMA, were only 9% of the Alu elements of the old J subfamily containing the SmaI site retain the AA dinucleotide needed for their amplification, while this figure is 91% and 80% in the younger AluY and AluS subfamilies respectively (Table 1), making clear that younger Alu elements tend to retain the SmaI site nearly as much as they retain the AA dinucleotide required for their amplification. This bias is not a handicap, since unmethylated Alu sequences revealed by AUMA are likely to represent the most relevant events of this kind, because spurious unmethylation of old Alu elements retaining a single or a few CpG sites is expected to have less biological significance than unmethylation of younger Alu elements that are usually closer to active chromatin regions (21) and retain more CpG‟s. The stronger methylation pressure observed in the AluY class is consistent with this postulate.
AUMA was designed to amplify DNA fragments containing the target sequence (AACCCGGG), which is present in Alu and other repetitive elements. Because a single primer was used for PCR amplification, the target sequence must appear in both strands of the DNA at relatively nearby positions. As expected, Alu elements, with more than 1 million copies per human genome (15), were the most frequent repeat in AUMA bands (50% in sequences isolated from non-tumor tissue), but only two sequenced bands contained two inverted Alu repeats (Supplemental Table 1). This observation is in concordance with previous works reporting on the instability of this inverted repeats, which might have caused their exclusion from the human genome (60,61). More restrictive conditions to select for Alu, or any other repeat of interest, may be achieved by extending the 3‟ end of the primer specific sequence (see supplemental data); however, the number of sequences we obtained was considered appropriate to accomplish the original aim of the study which is to screen for differentially methylated repetitive elements in colorectal cancer.
It is worth noting that AUMA patterns are highly reproducible not only in replicates but also among different samples, which indicates that the unmethylated status of these repeats is tightly controlled, probably by the epigenetic status of nearby regions. This is strengthened by the confirmation that unmethylation extends many CpG sites beyond the SmaI cut site. Moreover, about 50% of the bands tagged in AUMA fingerprints exhibited variable display among normal tissues (data not shown), suggesting the usefulness of this technique to investigate epigenetic polymorphisms.
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Alu‟s and other repetitive elements tend to be highly methylated in most somatic tissues (8,9,40,62). Here we have identified 78 “atypical” Alu elements exhibiting full or partial unmethylation in normal colonic mucosa cells. Different evidences underscore the adequacy of this approach to track changes with possible functional implications: (1) A significant portion of the characterized bands are located inside or nearby CpG islands and genes; (2) AUMA products show a characteristic distribution in R bands, coincidentally with the distribution of Alu sequences (15), indicating a bias toward the gene-richest portion of the genome known as the H3 isochore (63).
DNA methylation along Alu families
Alu families showed striking differences in their methylation level. Most of the Alu elements characterized here are from the younger families AluS and AluY (74% and 22%, respectively). Nevertheless this observation is mainly due to the depletion of CpG sites in older Alu elements. Hence, only 1 out 230 AluJ elements maintain the AUMA target site (AACCCGGG), while younger elements show higher rates of maintenance in accordance with their age (AluS: 1 out of 7; AluY: 2 out of 5) (Table 1). Interestingly, the rate of unmethylation is higher in older elements (AluJ: 12.2%; AluS: 3.1%; AluY: 1.6%) (Table 1). As noted by Rollins et al (21), the boundaries of unmethylated domains tend to be occupied by methylated Alu transposons of the younger AluS and AluY families. Other studies have noted that CpG island-associated Alu‟s retain a higher proportion of CpG sites, suggesting that these elements are unmethylated in the germ line (58). These unmethylated elements that can be easily revealed by AUMA are likely to play a regulatory role in a significant number of genes (64).
Application of AUMA to detect epigenetic changes in cancer cells
Cancer-related hypomethylation is well documented (reviewed in (23)) and different studies have demonstrated the demethylation of Alu‟s and other repetitive elements in different types of neoplasias (65). Our data are consistent with previous estimates and go one step forward in the characterization of unmethylated repeats. The AUMA approach was conceived as a straightforward DNA methylation screening strategy targeting specific interspersed repeats and suitable to be applied to large series of samples or experimental conditions as reported here.
The application of AUMA to a series of colorectal carcinomas and their paired matched normal tissue has revealed a high rate of alterations. This indicates the plasticity of epigenetic control of the elements screened by AUMA in colorectal carcinogenesis. Although some bands show bidirectional changes (hypomethylations and hypermethylations), which have been also reported in other sequences (42,66), most bands display an alternative trend either toward hypermethylations or hypomethylations. Some of these changes are highly recurrent (in more than 50% of tumors), suggesting that they may represent relevant alterations related to mechanisms frequently disturbed in colon cancer. Because the default status of repetitive elements is methylation, hypomethylations are readily detected as the emergence of a new band in the tumor AUMA fingerprint. Since all amplified bands include a unique sequence, it has been possible to identify all of the isolated bands and pinpoint them in the genomic map.
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As an example, we have investigated the Aq3 sequence, one of the most recurrent hypomethylations in this study. Aq3 band is flanked by two repeats, a LTR and an AluY, which map within an intron between exons 8 and 9 of the MYOM2 gene at 8p23.3. Both elements are heavily methylated in normal tissue and partially to fully unmethylated in tumor tissue. Interestingly, we have found moderately high levels of the heterochromatin associated mark 3mH3K9 in the fully methylated AluYd3 element in the HCT116 cell line, while the levels were significantly lower (3.5 fold) in the partially demethylated LoVo cell line. Furthermore, none of the classical active marks AcH3K9/K14 and 2mH3K79, have been found enriched in the LoVo cell line. These data are in concordance with preliminary data showing that the hypomethylation does not affect the expression of MYOM2 (data not shown) but could rather affect chromatin structure in the region. In agreement with these observations, this genomic region undergoes frequent losses (67-69) and is rearranged in many different types of tumors (70,71), which hints at a role for DNA hypomethylation in genomic instability (24-27,72). The specific functional consequences of this hypomethylation deserve further investigations.
Another application of AUMA is the detection of genomic regions that have been silenced in cancer. Interspersed elements are concentrated in gene-rich regions and due to the intended selection of unmethylated repetitive elements in AUMA, it appears reasonable to postulate that normally unmethylated sequences are likely to pinpoint active genomic regions. In this context, AUMA provides a large collection of genomic regions undergoing hypermethylation, which are readily seen as bands recurrently loss in the fingerprints. DNA methylation associated epigenetic silencing is probably one of the most prevalent mechanisms of tumor suppression inactivation in cancer (33,37,73). Therefore, AUMA can also be used to screen for differential methylation not only in Alu elements but also in unique sequences and repetitive elements other than Alu.
In summary, the QUMA and AUMA methodologies are a simple and novel approach to explore and gain insights into the functional significance of interspersed genomic elements and neighboring sequences. Due to its distinctive features (bias for unmethylated elements in gene- rich regions and detection of both hypomethylation and hypermethylation) we think that these techniques constitute a new and unique tool that should complement global determinations and high resolution genome-wide scanning strategies. Beyond unmethylated repetitive elements, AUMA can be also used to detect recurrent epigenetic changes associated with tumorigenesis including gene epigenetic inactivation.
Acknowledgements
We thank Gemma Aiza for technical support and Jessica Halow for critical review of the manuscript. JR was a fellow of the Generalitat de Catalunya; EV was a fellow of the Fondo de Investigación Sanitaria (FIS).
Resultats 87
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58. Brohede, J. and Rand, K.N. (2006) Evolutionary evidence suggests that CpG island- associated Alus are frequently unmethylated in human germline. Hum Genet., 119, 457-458. Epub 2006 Mar 2004. 59. Choi, I.S., Estecio, M.R., Nagano, Y., Kim do, H., White, J.A., Yao, J.C., Issa, J.P. and Rashid, A. (2007) Hypomethylation of LINE-1 and Alu in well-differentiated neuroendocrine tumors (pancreatic endocrine tumors and carcinoid tumors). Mod. Pathol., 20, 802-810. 60. Lobachev, K.S., Stenger, J.E., Kozyreva, O.G., Jurka, J., Gordenin, D.A. and Resnick, M.A. (2000) Inverted Alu repeats unstable in yeast are excluded from the human genome. EMBO J., 19, 3822-3830. 61. Stenger, J.E., Lobachev, K.S., Gordenin, D., Darden, T.A., Jurka, J. and Resnick, M.A. (2001) Biased distribution of inverted and direct Alus in the human genome: implications for insertion, exclusion, and genome stability. Genome Res., 11, 12-27. 62. Hellmann-Blumberg, U., Hintz, M.F., Gatewood, J.M. and Schmid, C.W. (1993) Developmental differences in methylation of human Alu repeats. Mol Cell Biol., 13, 4523-4530. 63. Saccone, S., Caccio, S., Kusuda, J., Andreozzi, L. and Bernardi, G. (1996) Identification of the gene-richest bands in human chromosomes. Gene, 174, 85-94. 64. Oei, S.L., Babich, V.S., Kazakov, V.I., Usmanova, N.M., Kropotov, A.V. and Tomilin, N.V. (2004) Clusters of regulatory signals for RNA polymerase II transcription associated with Alu family repeats and CpG islands in human promoters. Genomics., 83, 873-882. 65. Wilson, A.S., Power, B.E. and Molloy, P.L. (2007) DNA hypomethylation and human diseases. Biochim Biophys Acta., 1775, 138-162. Epub 2006 Sep 2001. 66. Nishiyama, R., Qi, L., Tsumagari, K., Weissbecker, K., Dubeau, L., Champagne, M., Sikka, S., Nagai, H. and Ehrlich, M. (2005) A DNA Repeat, NBL2, Is Hypermethylated in Some Cancers but Hypomethylated in Others. Cancer Biol Ther, 4, 4. 67. Emi, M., Fujiwara, Y., Nakajima, T., Tsuchiya, E., Tsuda, H., Hirohashi, S., Maeda, Y., Tsuruta, K., Miyaki, M. and Nakamura, Y. (1992) Frequent loss of heterozygosity for loci on chromosome 8p in hepatocellular carcinoma, colorectal cancer, and lung cancer. Cancer Res., 52, 5368-5372. 68. Sunwoo, J.B., Sun, P.C., Gupta, V.K., Schmidt, A.P., El-Mofty, S. and Scholnick, S.B. (1999) Localization of a putative tumor suppressor gene in the sub-telomeric region of chromosome 8p. Oncogene., 18, 2651-2655. 69. Muscheck, M., Sukosd, F., Pesti, T. and Kovacs, G. (2000) High density deletion mapping of bladder cancer localizes the putative tumor suppressor gene between loci D8S504 and D8S264 at chromosome 8p23.3. Lab Invest., 80, 1089-1093. 70. Wong, N., Wong, K.F., Chan, J.K. and Johnson, P.J. (2000) Chromosomal translocations are common in natural killer-cell lymphoma/leukemia as shown by spectral karyotyping. Hum Pathol., 31, 771-774. 71. Jin, Y., Jin, C., Wennerberg, J., Hoglund, M. and Mertens, F. (2001) Cytogenetic and fluorescence in situ hybridization characterization of chromosome 8 rearrangements in head and neck squamous cell carcinomas. Cancer Genet Cytogenet., 130, 111-117. 72. Karpf, A.R. and Matsui, S. (2005) Genetic disruption of cytosine DNA methyltransferase enzymes induces chromosomal instability in human cancer cells. Cancer Res., 65, 8635- 8639. 73. Egger, G., Liang, G., Aparicio, A. and Jones, P.A. (2004) Epigenetics in human disease and prospects for epigenetic therapy. Nature, 429, 457-463.
Resultats 91
1. Table Rest CpG islands AluY AluJ AluS Alu (S+J+Y) Total Sequence
7 6 perform calculations. genome(hg18 assembly, Mar. 2006). Twenty three additional bands weredetected inmainly tumor tissue and were not considered (174 hits)and 27 bands contributed only onedue to poo 5 4 3 2 hits. Only assembled chro 1
Number of occurrences of
Respect Respect the total number of Estimated number unmethylateof actualHits of AUMA products. Numberof AUMA hits presentin virtual productsPCR up of to 1000bp Elementsconsidered in theanalysis as obtained fromRepbase the and the Genome Browser Databases (seeMaterial and Methods) Genome Mb represented by each type of element. Total numbercorresponds to the number of megabases analyzed for thepresence
genome human hits in the AUMA and QUMA of and distribution Content
2836.9 3080.4 141.2 227.3 16.2 32.1 54.0 Mb
1
elements 1091110 1118195 mosome fragments wereconsidered. 147591 283104 660415 27085 No of
the sequence -
AA
Only bands appearing in normal tissue were considered. Eighty seven bands contributed two hits each
CCCGGG (or CCCGGGTT)hits.
2
d sites using Monte (CCCGGG) SmaI sites 239204 122459 198201 486835 49430 61725 14017
AA
CCCGGG (or CCCGGGTT)within each type of element.
AA
155226 168309
C 11410 56040 97951 CCCGGG
r sequenceor incomplete homology with the NCBI Build 36.1 ofhuman the 1673 1235 Hits arlo simulations 3
AUMA
. Virtual
1689 3382 5109 5498
326 (See Material and Methods) 63 38
hits
4
AUMA hits 87 87 (43.3%) 55 (27.4%) 45 (22.4%) 59 (29.3%) 12 12 (6.0%) 2 (1.0%)2
201
5
Unmethyl
. 14332 ± 2418
8530 8530 ± 1 3028 3028 ± 510 4104 ± 688 1501 1501 ± 97 925 ± 156 151 ± 25 Hits 6
650 ated
Unmethylated 75.9 ± 14.63% 12.25 1.97%± 90.5 ± 5.79% 1.65 ± 0.27% 3.09 ± 0.51% 2.64 ± 0.44% 8.52 ± 1.4% Hits (%)
7
to
of of
Resultats 92
Table 2. Table Au4 c1 c6As3 Ar3 c3 c6Aq3 c6Ap1 Ao1 c4 c1Aj2 Ai1 c3 Band ID
c CpG island. b a
As compared to the paired normal tissue.
Nucleotide position within the contig (strand +). NCBI Build 36.1 of the human genome.
The whole sequence or a fragment of the sequence lays not further than bp of 200 a predicted
Size (bp)
A selection of characterized AUMA bands characterized of A selection
268 306 329 339 358 365 458 509
% GC
57 63 57 58 56 50 49 55
16p13.3 (3162099 16p13.3 (31604 2q14.3 (127875178 8p23.3 (2007343 5q35. 19q13.32 (53550179 1q32.2 (206389082 17p11.2 Chromosome map (Location 2 (175157321
(18206453
77 -
- - 2007682)
- - - - 3162366) 3160782)
- 127875506) 175157674) 206389539) 18206961)
53550543) a )
None None AF370412 MYOM2 CPLX2 AK001784 MGC29875 SHMT1 Gene
No Yes Yes No Yes No Yes Yes CpG island
b
tRNA tRNA None/ None None / None MIRb / AluLTR / Y None MIR / Alu Sx MIRb / Alu None Sq / Alu Sx MIR / band ends (5‟/3‟) Repetitive elements in
Hypermethylated Hypermethylated Hypomethylated Hypomethylated Hypermethylated Hypomethylated Hypomethylated Hypermethylated in tumor Methylation status
c
Resultats 93
FIGURE LEGENDS
Figure 1. Schematic diagram of the QUMA and AUMA methods. DNA is depicted by a solid line, Alu elements are represented by dashed boxes. The QUMA and AUMA recognition sites (AACCCGGG) are represented by dashed/gray boxes. CpGs at SmaI sites are shown as full circles when methylated and as open circles when unmethylated. The methylation-sensitive restriction endonuclease SmaI can only digest unmethylated targets, leaving blunt ends to which adaptors can be ligated. (A) QUMA is performed by real time PCR of an inner Alu fragment using a primer complementary to the Alu consensus sequence upstream of the SmaI site and the primer complementary to the adaptor to which two Alu homologous nucleotides (TT) have been added. (B) In AUMA, sequences flanked by two ligated adaptors are amplified by PCR using a single primer, the same adaptor primer plus the TT nucleotides. When only a few nucleotides are added to the primer, i.e. TT, as illustrated here, other non Alu sequences may be amplified. This allows the amplification of a large number of sequences that typically range from 100 to 2000 bp.
Figure 2. AUMA of normal (N)-tumor (T) pairs of two different patients performed using primer BAu-TT. A highly reproducible band patterning is observed among the four replicates. Representative bands showing gains (hypomethylations) and losses (hypermethylations) are marked with up and down arrowheads respectively.
Figure 3. Relative distribution the Alu elements and sequence targets considered in bioinformatic and experimental QUMA and AUMA. Mb: Number of megabases occupied by each type of element; Elements: Number of elements considered (“Rest” has been set arbitrarily to 50%); SmaI site: CCCGGG sequence; vQUMA hits: AACCCGGG (or GGGCCCTT) sites in Alu elements; vAUMA hits: AACCCGGG (or GGGCCCTT) sites; vAUMA ends: vAUMA hits considering only putative AUMA products of less than 1 Kb (see Material and Methods); AUMA: elements at each one of the two ends of actual AUMA products.
Figure 4. Quantitation of unmethylated Alu‟s in 17 paired normal mucosa and colorectal carcinoma by QUMA. The values represent the estimated number of unmethylated Alu‟s per haploid genome. Most tumors exhibited a higher level of hypomethylation when compared with the respective normal.
Figure 5. (A) Chromosomal origin of AUMA products. A competitive hybridization of AUMA product obtained from normal tissue DNA (red) and genomic DNA (green) to metaphase chromosomes was performed. AUMA products showed an unequal distribution along chromosomes, displaying highest densities at most telomeric regions and some interstitial bands. Chromosomes 16, 17 and 19 yielded the highest AUMA density. (B) Intensity distribution of AUMA products hybridized to BAC arrays in selected chromosomes. The average intensity (X axis) of the two normal (blue) and tumor samples analyzed (red) for each BAC is shown. BACs are arranged along the Y axis according to its position in the chromosome. (C) Differential methylation profiles determined by competitive hybridization of AUMA products from normal and tumor tissue to BAC arrays. Illustrative examples are shown for chromosomes 7 and 8 from
Resultats 94 the two cases analyzed (81 and 151). X axis indicates log2 ratio of Tumor/Normal intensities. Positive values (to the right) indicate hypomethylations, negative values (to the left) indicate hypermethylations. Additional examples are shown in Supplemental figure 5.
Figure 6. Distribution of hypermethylation and hypomethylation rates in the 110 AUMA tagged bands. Rates were obtained by comparison of the AUMA fingerprints obtained in 50 colorectal tumors as compared to their respective matched normal tissue.
Figure 7. (A) Detail of the AUMA fingerprints generated from 5 normal-tumor sample pairs. The presence of the Aq3 band is indicated by an asterisk under the three Aq3 positive cases. (B) The relative position of the AUMA Aq3 band, MLT1A and Alu Y repetitive elements, as well as MYOM2 ninth exon are shown. Each vertical line in the CpG distribution represents a CpG dinucleotide along the DNA sequence. Two different fragments were amplified for the bisulfite sequencing analysis (gray boxes). Sequence is oriented 5‟ to 3‟ in regard to MYOM2 3‟ end. (C) Methylation status of the CpG nucleotides in the two fragments amplified were ascertained by direct sequencing of bisulfite treated DNAs of 5 normal-tumor pairs and 5 colon cancer cell lines. (D) ChIP analysis of the AluY element frequently hypomethylated in cancer revealed loss of trimethylation in histone 3 lysine 9 residue (3mH3K9) in LoVo cells (unmethylated at DNA level) as compared to HCT116 (methylated at DNA level). Treatment of HCT116 cells with 5AzaC and TSA produced a moderate decrease in the levels of trimethylation in H3K9.
Resultats 95
Quantification of UnMethylated Alu (QUMA) A UnMethylated Alu Methylated Alu
SmaI
Adaptor ligation
Purification Real time PCR amplification
Amplification of UnMethylated Alu (AUMA) B UnMethylated Alu Methylated Alu
SmaI
Adaptor ligation
Purification PCR amplification
Alu product Non Alu product
Gel electrophoresis AACCCGGG
FIGURE 1
Resultats 96
1 2 NNNN TTTT NNNN TTTT
FIGURE 2
Resultats 97
100% 90% 80% 70% CpG islands 60% AluY 50% AluJ 40% AluS Rest 30% 20% 10% 0% Mb Elements SmaI vQUMA vAUMA vAUMA AUMA site hits hits ends
FIGURE 3
Resultats 98
80,000 normal tumor 70,000
60,000
50,000
40,000
30,000
20,000
10,000
Unmethylated alu/haploid genome alu/haploid Unmethylated 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
Case ID
FIGURE 4
Resultats 99
A
10 20 30
10 20 30
10 20 30
10 20 30
10 20 30
10 20 30
0
0
0
0
0
0 B
ch 19
10 20 30
0
T 20Mb N
T ch 1 ch 2 ch 3 ch 7 ch 8 ch 16 N
C
-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3 81 ch7 151 ch7 81 ch8 151 ch8
FIGURE 5
Resultats 100
0.8
0.6
0.4
Hypermethylation rate Hypermethylation
0.2 0.0
0.0 0.2 0.4 0.6 0.8
Hypomethylation rate
FIGURE 6
Resultats 101
FIGURE 7
Resultats 102
Supplemental Data
Supplemental METHODS
QUANTIFICATION OF UNMETHYLATED ALU (QUMA)
ALU consensus sequence around the SmaI site (up and down DNA strands) unmethylated SmaI site 5‟-...GGAGAATCGCTTGAACCC ↓ GGGAGGCGGAGGTTGCAG...-3‟ 3‟-...CCTCTTAGCGAACTTGGG ↑ CCCTCCGCCTCCAACGTC...-5‟
Adaptor Primer name P-5‟-TCAGAGCTTTGCGAAT-3‟ 5PMCF 3‟-AGTCTCGAAACGCTTAAGCC-5‟ Blue
Ligated fragment and upstream and downstream QUMA primers (green captions)
5’-CCGTCTCTACTAAAAATACA-3’ (Alu1) 5‟-AACCCCGTCTCTACTAAAAATACAAAAATTAGCCGGGCGTGGTGGCGCGCGCCTGTAATCC.. 3‟-TTGGGGCAGAGATGATTTTTATGTTTTTAATCGGCCCGCACCACCGCGCGCGGACATTAGG..
..CAGCTACTCGGGAGGCTGAGGCAGGAGAATCGCTTGAACCCTCAGAGCTTTGCGAAT-3‟ ..GTCGATGAGCCCTCCGACTCCGTCCTCTTAGCGAACTTGGGAGTCTCGAAACGCTTAAGCC-5‟ 3’TTGGGAGTCTCGAAACGCTTA-5‟(BauTT)
QUMA Normalization Primers (5‟-3‟): CCGTCTCTACTAAAAATACA, ATTCTCCTGCCTCAGCCT A consensus Alu and the control region amplified to normalize QUMA is shown bold. Primer sites are shown in green. SmaI site is shown in blue.
GGCCGGGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCGGGCGGATCACCTGA GGTCAGGAGTTCGAGACCAGCCTGGCCAACATGGTGAAACCCCGTCTCTACTAAAAATACAAAAAT TAGCCGGGCGTGGTGGCGCGCGCCTGTAATCCCAGCTACTCGGGAGGCTGAGGCAGGAGAA TCGCTTGAACCCGGGAGGCGGAGGTTGCAGTGAGCCGAGATCGCGCCACTGCACTCCAGCCTGGGCG ACAGAGCGAGACTCCGTCTCAAAAAAAA
QUMA standard Genomic fragment used to generate the QUMA standard. Fragment was generated by PCR from genomic DNA (locus chr17:18,206,278-18,206,720) using the primers: GTTACCCAGAGAATAGACTG, AAAACACAATTACCCACTTCC.
Alu Sx sequence is shown in lower case, unique sequence is shown in uppercase:
GTTACCCAGAGAATAGACTGGAAACACCAGTTTAGGTTCTTGCAGAAATAACATTGTAggccgggcgcg gtggctcatgcctgtaatcccagcattttgggaggccgaggtgggcggatcacttgaggttagtagttggagaccagcctggccaacat ggtgaaaccccgtctgtactaaaaatacaaaaaaacctggctgcccatggtggcacacacctataatcccagctactcggaaggctga ggcaggagaatcgcttgaacccgggaggtggaggttgcagtgagcggagatcacgccactgcactccagcctgagtgacagagtg agactctatctcaaaagaaagaaagagagagaaagaaaagaaAAAAAAGAAAAGAAATAACGTAAATTTATTACTTG GGGGAACTTGGAACGGAAGTGGGTAATTGTGTTTT
Resultats 103
AMPLIFICATION OF UNMETHYLATED ALU (AUMA)
Consensus Alu and AUMA primer sequences Accession numbers of consensus Alu sequences: Alu J (U14567); Alu Sb (U14568); Alu Sb1 (U14569); Alu Sc (U14571); Alu Sp (U14572); Alu Sq (U14573); Alu Sx (U14574) Available from GeneBank database at http://ncbi.nih.gov/
Human consensus ALU interspersed repetitive sequence (SmaI site is highlighted) obtained with ClustalW software (European Bioinformatics Institute, EMBL-EBI at http://www.ebi.ac.uk/clustalw/)
GGCCGGGCGCGGTGGCTCACGCCTGTAATCCCAGCACTTTGGGAGGCCGAGGCGGGCGGATCACCTGA GGTCAGGAGTTCGAGACCAGCCTGGCCAACATGGTGAAACCCCGTCTCTACTAAAAATACAAAAATTAG CCGGGCGTGGTGGCGCGCGCCTGTAATCCCAGCTACTCGGGAGGCTGAGGCAGGAGAATCGCTTGAAC CCGGGAGGCGGAGGTTGCAGTGAGCCGAGATCGCGCCACTGCACTCCAGCCTGGGCGACAGAGCGAG ACTCCGTCTCAAAAAAAA
ALU consensus sequence around the SmaI site (up and down DNA strands) unmethylated SmaI site 5‟-...GGAGAATCGCTTGAACCC ↓ GGGAGGCGGAGGTTGCAG...-3‟ 3‟-...CCTCTTAGCGAACTTGGG ↑ CCCTCCGCCTCCAACGTC...-5‟
Adaptor sequence (two views of the same adaptor are shown as ligated to the Alu tail or the head) Primer name Tail-ligation 5‟-CCGAATTCGCAAAGCTCTGA-3‟ Blue 3‟-TAAGCGTTTCGACACT-5‟-P 5PMCF
Head-ligation P-5‟-TCAGAGCTTTGCGAAT-3‟ 5P-MCF 3‟-AGTCTCGAAACGCTTAAGCC-5‟ Blue
Ligated fragment and Alu downstream PCR primers (green captions)
5‟-ATTCGCAAAGCTCTGAGGGAGGCGGA-3‟ BAd-AGGCGGA 5‟-ATTCGCAAAGCTCTGAGGGAGGC-3‟ BAd-AGGC 5‟-ATTCGCAAAGCTCTGAGGGAG-3‟ BAd-AG 5‟-CCGAATTCGCAAAGCTCTGAGGGAGGCGGAGGTTGCAG...-3‟ 3‟-TAAGCGTTTCGACACTCCCTCCGCCTCCAACGTC...-5‟
Ligated fragment and Alu upstream PCR primers (green captions)
5‟-...GGAGAATCGCTTGAACCCTCAGAGCTTTGCGAAT-3‟ 3‟-...CCTCTTAGCGAACTTGGGAGTCTCGAAACGCTTAAGCC-5‟ 3‟-TTGGGAGTCTCGAAACGCTTA-5‟ BAu-TT 3‟-ACTTGGGAGTCTCGAAACGCTTA-5‟ BAu-TTCA 3‟-CGAACTTGGGAGTCTCGAAACGCTTA-5‟ BAu-TTCAAGC
Resultats 104
QUMA PCR conditions
Amplification was performed using 1 ng of the ligated product in a reaction volume of 10 μl. Mastermix (Roche) was prepared to a final concentration of 3.5 mM MgCl2 and 1 μM of each primer. PCR amplification consisted of 35 three-step cycles (10 s at 95 ºC, 20 s at 65ºC and 30 s at 72ºC), preceded by a denaturation step of 10 min at 95ºC.
AUMA PCR conditions
Amplification was performed using 3 μl of the ligated product (~12ng) in a reaction volume of 25 μl containing 0.8 μM of primer, 2U Taq DNA polymerase (Roche Diagnostics), 100 μM of each dNTP, and PCR buffer (10mM Tris-HCl pH 8.0, 50mM potassium chloride, 1.5mM MgCl2).
MgCl2 was added to the PCR mix to a final concentration of 2.3mM. PCR conditions were identical for all primers and consisted of 35 three-step cycles (30 s at 95ºC, 30 s at 65ºC and 1 min at 72ºC). PCR cycles were preceded by denaturation for 1 min at 95ºC and ended with an extension step of 5 min at 72ºC. Reactions were performed in a Programmable Thermal Controller PTC100 (MJ Research Inc., Watertown, MA). The PCR products were diluted 1:4 in formamide dye buffer and denatured for 3 min at 95ºC. Three µl of denatured product were run on a 6% polyacrylamide 8M urea sequencing gel at 55W for 6 h. Initial experiments were visualized by silver staining of the gels. To improve the overall quality of fingerprints in normal- tumor comparisons, radioactive AUMA‟s were performed. One μCi α-32P-dCTP (Amersham Biosciences) was added to the PCR mix. The gels were dried under vacuum at 85ºC and exposed to an X-ray film at room temperature without an intensifier screen for 3-6 days.
Resultats 105
Supplemental Table 1. Sequences isolated from AUMA (part 1)
Size SmaI site in band Repetitive elements Band ID % GC Chromosome map (Locationa) Gene (bp) ends in band ends (5’/3’) AU-66 405 52 None / Sma I 1p36.12 (21835410-21835814) RAP1GA1 MIRb / Alu Sp AU-82 137 63 Sma I / Sma I 1q21.1 (147553872-147554008) AK124550b None / tRNA AU-18 186 49 Sma I / Sma I 1q21.1 (147561130-147561315) None None / tRNA AU-168 137 61 Sma I / Sma I 1q21.1 (147939418-147939554) Noneb None / tRNA AU-47 186 46 Sma I / Sma I 1q21.1 (147946674-147946859) None None / tRNA AU-136 603 47 None / Sma I 1q21.3 (151471158-151471495) None None / None Ar7 c6 322 57 Sma I / Sma I 1q23.2 (156829373 – 156829688) KCNJ10 None / None AU-72 206 66 Sma I / Sma I 1q23.3 (159334967-159335172) KARCA1b None / None AU-131 266 47 Sma I / None 1q23.3 (159767613-159767838) None None / None AU-48 400 45 Sma I / Sma I 1q24.2 (165950975-165951374) Noneb None / tRNA AU-45 792 33 Sma I / None 1q25.2 (174982256-174983047) PAPPA2 Alu Y / None Aj2 c1 464 49 Sma I / Sma I 1q32.2 (206389079 – 206389542) MGC29875b Alu Sq / None AU-96 464 50 Sma I / Sma I 1q32.2 (208067307-208067770) Noneb Alu Sq / None AU-64 438 51 Sma I / Sma I 1q32.3 (209975376-209975807) None None / None AU-22 495 54 Sma I / Sma I 1q41 (219027406-219027900) MOSC1b None / Alu Sx AU-57 210 56 Sma I / None 2p11.2 (88172667-88172870) SMYD1 LTR33 AU-84 598 55 Sma I / Sma I 2p11.2 (85846392-85846989) ATOH8 None / None Ad3 c1 795 56 Sma I / Sma I 2p23.3 (27096094 – 27096888) None None / Alu Y AU-23 195 59 Sma I / Sma I 2p24.1 (23712291-23712485) BC069271 / AL834515 None / None AU-113 989 52 None / Sma I 2p25.1 (10076680-10077668) None L1MA1 / None AU-164 506 32 None / Sma I 2q13 (112505156-112505661) None None Al1 c2 423 52 Sma I / Sma I 2q14.2 (121559187 – 121559609) None Alu Y / None Ar3 c3 335 57 Sma I / Sma I 2q14.3 (127875175 – 127875509) AF370412b None / MIRb At3 c3 288 52 Sma I / Sma I 2q21.1 (130817182 – 130817463) IMP4b None / Alu Sg AU-8 388 54 Sma I / Sma I 2q21.1 (130817422-130817709) IMP4b None / Alu Sg Ar3 c1 334 51 Sma I / Sma I 2q31.2 (180028122 – 180028455) None Alu Sx / None AU-65 437 58 Sma I / Sma I 2q35 (219433280-219433716) WNT6b None / None AU-114 322 53 Sma I / Sma I 2q37.3 (237777804-237778125) None Alu Sx / None AU-40 263 56 Sma I / Sma I 3p22.1 (40631798-40632060) Noneb None / None AU-33 393 53 Sma I / Sma I 3q26.2 (170347518-170347910) EV11 None / None Ao1 c2 373 60 Sma I / Sma I 4q31.3 (154527715 – 154528087) TRIM2b None/None AU-105 570 44 Sma I / None 4q35.2 (190917251-190917820) None None / None Ah1 c3 575 44 Sma I / Sma I 4q35.2 (191055406 – 191055980) None None / None AU-86 320 43 None / Sma I 5p12 (45741040-45741359) None None / Alu Y AU-135 380 46 None / Sma I 5p13.3 (33041752-33042131) None MER4B / Alu Y AU-180 316 50 Sma I / None 5q33.1 (148845290-148845605) None Alu Jb / None Ae2 c6 717 47 Sma I / Sma I 5q33.1 (151709944 – 151710660) None None / Alu Sx Ar3 c7 335 56 Sma I / Sma I 5q35.1 (167846537 – 167846876) RARS None / Alu Y AU-166 984 53 Sma I / Sma I 5q35.1 (171911500-171912483) None Alu Sc / Charlie 7 Ap1 c6 364 56 Sma I / Sma I 5q35.2 (175157319 – 175157677) CPLX2b None / MIR AU-109 525 47 None / Sma I 6p12.3 (48557303-48557827) None L1MA3 / MER1B AU-117 198 46 Sma I / None 6q13 (70235881-70236078) None HERV9 AU-79 164 60 Sma I / Sma I 6q21 (107887407-107887570) PDSS2b None / None AU-125 307 41 Sma I / None 6q22.33 (130283187-130283493) None None / MLT1H1 AU-30 460 51 Sma I / Sma I 6q27 (165197147-165197606) None L1Mda / Alu Sq Aq1 c1 354 53 Sma I / Sma I 6q27 (167075092 – 167075445) RP6SKA2 None / None Au1 c1 279 49 Sma I / Sma I 7p14.2 (35944501- 35944769) None LINE L2 / Alu Yd2 AU-50 268 52 Sma I / Sma I 7p15.2 (25857505-25857772) None None / None Ah2 c10 563 48 Sma I / Sma I 7p22.2 (2893157 – 2893719) None LTR / Alu Sc AU-167 234 59 Sma I / Sma I 8p23.1 (12508845-12509078) Noneb None / None Av3 c1 245 49 Sma I / Sma I 8p23.2 (2994895 – 2995136) None None / None Aq3 c6 345 58 Sma I / Sma I 8p23.3 (2007340 – 2007685) MYOM2 LTR / Alu Y Av2 c2 242 52 Sma I / Sma I 8q24.11 (117883300 – 117883541) None Alu Y / LTR AU-140 423 49 None / Sma I 8q24.22 (132120338-132120760) ADCY8 None / None AU-41 444 57 Sma I / Sma I 8q24.3 (142607517-142607960) None None / None
Resultats 106
Supplemental Table 1. Sequences isolated from AUMA (part 2)
Size Sma I targets in Repetitive elements Band ID % GC Chromosome map (Locationa) Gene (bp) band ends (5’/3’) in band ends (5’/3’) AU-187 302 51 Sma I / None 9p11.1 (47044445-47044746) None Alu Sp / None Ag2 c5 615 55 Sma I / Sma I 9p13.1 (38683335 – 38683950) None Alu Y / None AU-16 603 58 Sma I / Sma I 9p24.2 (2231367-2231969) Noneb None / None Ao1 c0 345 45 Sma I / Sma I 9q21.31 (79626081 – 79626425) None Alu Sx / Alu Jo AU-68 310 55 Sma I / Sma I 9q22.1 (89779683-89779992) Noneb None / Alu Sg AU-150 306 54 Sma I / Sma I 9q34.11 (129597457-129597762) None AluY AU-61 361 46 None / Sma I 10q21.1 (53837803-53838163) None L2 / Alu Sc AU-99 448 54 Sma I / Sma I 10q26.11 (120504982-120505429) Noneb None / Alu Y Ar7 c5 324 55 Sma I / Sma I 10q26.13 (125843458 – 125843781) None None / Alu Sp AU-1 448 59 Sma I / Sma I 11q12.1 (59089995-59090442) None None / None AU-100 280 52 Sma I / Sma I 11q13.1 (64748856-64749135) FLJ16331 Alu Sc / MIR AU-108 327 51 Sma I / Sma I 11q13.2 (68668053-68668379) None Alu Sc / MER74A Ao1 c1 333 45 Sma I / Sma I 11q13.4 (73980931 – 73981257) POLD3b MER44A / None AU-155 219 53 Sma I / Sma I 11q21 (95763104-95763322) Noneb None / None AU-17 668 54 Sma I / Sma I 11q23.3 (116164068-116164735) Noneb None / None An1 c2 390 43 Sma I / Sma I 11q23.3 (116395630 – 116396019) KIAA0999 Alu Sc / None Ao2 c1 371 46 Sma I / Sma I 11q24.3 (129899206 – 129899576) None LINE L2 / Alu Sg/x Ar3 c5 334 49 Sma I / Sma I 11q24.3 (130094098 – 130094431) None Alu Sx / None AU-179 160 62 Sma I / Sma I chr11 (not mapped) None None / None AU-157 629 43 Sma I / None 12p12.1 (25467561-25468189) None Alu Sq / None Aj1 c2 462 57 Sma I / Sma I 12p13.31 (6527527 – 6527992) HOM-TES-103b None / None AU-10 673 43 None / Sma I 12p13.32 (3851608-3852280) PARP11 MER39 / Alu Sg/x Ar5 c2 327 59 Sma I / Sma I 12q13.11 (47397075 – 47397401) CCNT1b None / Alu Sg Ai2 c5 511 39 Sma I / Sma I 12q21.31 (84722784 – 84723294) PAMCI Alu Y / None Ai1 c1 520 51 Sma I / Sma I 12q24.11 (109686793 – 109687312) None None / Alu Sx AU-13 249 53 None / Sma I 12q24.32 (124864438-124864686) None MERSA / Alu Sq AU-127 328 38 Sma I / None 12q24.32 (126451330-126451657) None Alu Sx / L1ME1 AU-92 491 50 Sma I / Sma I 13q12.13 (26373474-26373964) None None / Alu Y Ah2 c8 563 50 Sma I / Sma I 13q21.32 (64681523 – 64682085) None Alu Y / LTR AU-174 617 46 Sma I / Sma I 14q32.2 (97438753-97439369) None None / Alu Y AU-80 303 46 Sma I / Sma I 15q11.2 (18704809-18705111) None None / Alu Sq Aj1 c9 468 52 Sma I / Sma I 15q12 (23575207 – 23575674) ATP10A Alu Sx / None Aj1 c6 467 44 Sma I / Sma I 15q22.2 (58086012 – 58086478) None b None / None AU-44 268 63 Sma I / Sma I 15q22.31 (63949311-63949578) RAB11Ab None / None Ao1 c6 374 53 Sma I / Sma I 15q23 (68573371 – 68573744) None None / MIR AU-20 249 43 Sma I / Sma I 15q24.3 (74421623-74421871) ISL2b None / None Al1 c6 428 49 Sma I / Sma I 15q26.3 (96971519 – 96971946) None Alu Sq / None AU-60 460 49 Sma I / Sma I 16p11.2 (31100903-31101362) FUS Alu Sx / None Ac4 c1 903 54 Sma I / Sma I 16p12.1 (27944929 – 27945831) AY358206 None / Alu Sx AU-34 434 52 Sma I / Sma I 16p13.13 (11742883-11743316) TXNDC11 L2 / Alu Sx AU-112 204 44 None / Sma I 16p13.2 (8523674-8523877) None None / None As3 c6 312 63 Sma I / Sma I 16p13.3 (3160474 – 3160779) None c None / None Au4 c1 274 57 Sma I / Sma I 16p13.3 (3162096- 3162369) None tRNA / None AU-56 619 51 Sma I / Sma I 16p13.3 (3179681-3180299) Noneb tRNA / None AU-126 315 54 Sma I / Sma I 16p13.3 (392266-392580) DECR2b Alu Sx / None AU-104 504 58 Sma I / Sma I 16p13.3 (4466925-4467428) HMOX2b None / Alu Sx Ah2 c6 563 52 Sma I / Sma I 16q21.1 (85551517 – 85552079) None None / None Ai1 c3 515 55 Sma I / Sma I 17p11.2 (18206450 – 18206964) SHMT1b Alu Sx / MIR AU-35 247 51 Sma I / None 17q21.31 (38831114-38831360) None Alu Sx / MIRb AU-73 279 52 None / Sma I 17q22 (53031500-53031778) MSI2 None / None AU-55 480 49 Sma I / Sma I 17q24.3 (67555550-67556026) None None / Alu Sg Ar7 c1 323 56 Sma I / Sma I 17q25.3 (73693981 – 73694303) TK1 None / None Ah1 c1 566 49 Sma I / Sma I 18p11.23 (7492459 – 7493024) None None / Alu Sg AU-152 401 55 Sma I / Sma I 18p11.31 (3442451-3442851) TGIFb None / None At1 c8 295 38 Sma I / Sma I 18q21.2 (51649154- 51649432) None None / None AU-159 667 50 Sma I / Sma I 18q21.31 (53256789-53257455) ONECUT2b None / None Al1 c4 429 58 Sma I / Sma I 18q22.3 (70314985 – 70315413) CNDP2 b None / None
Resultats 107
Supplemental Table 1. Sequences isolated from AUMA (part 3)
Size Sma I targets in Repetitive elements Band ID % GC Chromosome map (Locationa) Gene (bp) band ends (5’/3’) in band ends (5’/3’) AU-186 419 59 Sma I / Sma I 19p13.11 (19600679-19601097) Noneb None / Alu Sc AU-29 279 53 Sma I / Sma I 19p13.13 (13165698-13165976) None Alu Sx / Alu Sg/x As3 c5 312 58 Sma I / Sma I 19p13.2 (9799858 - 9800169) UBL5b None / None AU-149 549 53 Sma I / Sma I 19p13.3 (1970712-1971260) None None / Alu Y At1 c3 295 59 Sma I / Sma I 19p13.3 (5273523 - 5273811) None None / Alu Sp AU-24 250 48 Sma I / Sma I 19q13.2 (45624073-45624322) Noneb MIRb / MIR AU-101 498 48 Sma I / Sma I 19q13.2 (46810414-46810911) None Alu Sx / None Ao1 c4 371 50 Sma I / Sma I 19q13.32 (53550176 - 53550540) AK001784 Alu Sx / MIRb Aj1 c1 461 56 Sma I / Sma I 19q13.33 (54885550 - 54886010) CPT1Cb None / None AU-162 398 49 Sma I / Sma I 19q13.43 (61982532-61982929) ZIM2 None / MER20 AU-2 251 58 Sma I / Sma I 20q11.23 (34926178-34926428) Noneb None / Alu Sg Ak1 c7 434 53 Sma I / Sma I 20q13.12 (42690824 - 42691257) ADA None / None AU-43 395 57 Sma I / None 20q13.13 (45894701-45895095) None MLT2B4 / None AU-124 298 52 Sma I / Sma I 20q13.31 (55628389-55628686) ZBP1 Alu Sx / None AU-97 540 58 Sma I / Sma I 20q13.33 (61126250-61126789) None None / None AU-133 398 56 Sma I / Sma I 21q22.13 (37004089-37004486) SIM2 Alu Sx / None AU-130 682 56 Sma I / Sma I 21q22.3 (43449720-43450401) None Alu Sg/x / None AU-139 459 44 None / Sma I 22q11.1 (15411531-15411988) None ERVL-B4 / ERVL-B4 AU-106 432 53 Sma I / Sma I 22q12.3 (35742025-35742452) TST Alu Sg / None Al1 c1 426 52 Sma I / Sma I 22q12.3 (35736578 - 35737003) TST Alu Sg / None AU-14 290 50 Sma I / Sma I Xp22.13 (17472645-17472934) NHS Alu Sx / None AU-19 689 47 None / Sma I Xq11.2 (65039112-65039800) None None / Alu Sg AU-121 328 44 Sma I / Sma I Xq13.3 (75267216-75267543) None LTR17 / L1PA11 AU-138 880 50 Sma I / Sma I Yp11.2 (10493777-10494656) None LTR1 / None AU-51 629 48 Sma I / Sma I Yp11.31 (1736039-1736667) None Alu Sx / None
a Nucleotide position within the contig (strand +). NCBI Build 36.1 of the human genome. b The whole sequence or a fragment of the sequence lays not further than 200 bp of a predicted CpG island.
Resultats 108
Supplemental Table 2. Bisulfite sequencing and gene expression primer sequences
Purpose Primer Sequence (5’-3’) Aq3 band (MLT1A element), bisulfite sequencing, first PCR GTTGGATTTAAATTTTTGGTT TTAACAACAAACACACTCAA Aq3 band (MLT1A element), bisulfite sequencing, nested PCR GATTAGGTTTTTAGTTTTGT CCCACAACATAAAACACC Aq3 band (AluY element), bisulfite sequencing, first PCR GTGTTTTATGTTGTGGGG CCCAAACTTCTCCAAAAACTA Aq3 band (AluY element), bisulfite sequencing, nested PCR ATTTGAGTGTGTTTGTTGTT ATAACAAAAACACTACACAA Aq3 band (AluY element), ChIP analysis GTGGGGCGGTGACAAAGACG AGGGGCACTGCACAGAAACGAT
Resultats 109
Supplemental Figures
A
B
C
14 23 LC114 21 LC116 13 D 19 LC119 y = -2.4559Ln(x) + 44.427 2 12 17 R = 0.9866 y = -2.3798Ln(x) + 43.05 2 y = -1.6064Ln(x) + 21.033 15 R = 0.9865 11 2
Crossing Point Crossing R = 0.9838 y = -2.1713Ln(x) + 40.182 Point Crossing 13 y = -1.4802Ln(x) + 20.241 R2 = 0.9931 10 R2 = 0.9978 11
9 9 1000 10000 100000 1000000 100 pg of DNA / tube 1000 Alus / tube
Supplemental Figure 1
Real time analysis of QUMA amplification and melting curves. (A) QUMA analysis of the AluSx sequence used as standard in concentrations ranging from 104 to 107 molecules per reaction tube. Due to the presence of a unique Alu sequence, melting curves show a single peak. (B) QUMA analysis of the AluSx sequence used as standard (equivalent concentrations ranging from 104 to 107 molecules per ng of DNA were added) spiked on a genomic DNA digested with XmaI. Sensitivity was in the range of 104 molecules per ng of DNA. (C) QUMA analysis of normal and tumor samples. Melting curves show multiple peaks due to the presence of different Alu elements. (D) Three QUMA dilution curves in three independent experiments (left) and QUMA dilution curves of two sample DNAs (right). In all cases analysis was performed in duplicate. The linearity of the response is observed in standard and sample dilutions.
Resultats 110
N N T T N N T T N N T T N N T T N N T T N N T T N N T T
Size
600 510
360
310
270
200
Supplemental Figure 2
Representative fingerprints of AUMA products obtained with primers amplifying towards Alu promoter (BAu-TT, BAu-TTCA and BAu-TTCAAG) and towards Alu poly-A tail (BAd-AG, BAd-AGGC, BAd-AGGCGG).
Duplicate analysis of normal-tumor pairs was performed. One case is shown for each primer except for Bau-TT, for which two cases are illustrated. Approximate band size (bp) is shown at the left. Gels were silver stained for band visualization.
Resultats 111
Sample A Sample B 1 2 3 1 2 3
Supplemental Figure 3
Interassay reproducibility of AUMA. Two sample DNAs (A and B) were analyzed in three different experiments (1 to 3). PCR was performed in duplicate for each replica, run in denaturing polyacrylamide gel and silver stained. While most bands maintained the display among replicas, a few bands (marked with and arrowhead) showed inconsistent display in both intraassay and interassay replicates. Bands with irreproducible display were not considered for further analysis and were not included in the accounting of normal-tumor differences.
Resultats 112
A B N N T T N N T T N N T T
Ap1 Aq3
Ap1 control
control Aq3
C D N N T T N N T T N N T T N N T T
Ah1 Ai1
Ai1 Ah1 control control
Supplemental Figure 4
Southern Blot confirmation of AUMA isolated bands. Two hypomethylated (A and C) and two hypermethylated AUMA bands (B and D) were labeled by PCR amplification in the presence of [α-32P]dCTP. Top of each panel corresponds to the silver stained AUMA fingerprint that harbors the band to be tested while bottom of each panel corresponds to the AUMA gel blotted to a nitrocellulose membrane hybridized with the band of interest. For each hybridization a positive control consisting in the isolated band that had to be hybridized was included.
Resultats 113
A 81T 151T
B
chr 1
-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3
chr 5
-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3
chr 9
-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3
chr 11
-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3
Supplemental figure 5
(A) CGH ideograms of two tumors are shown. (B) Comparison of the genomic profile of selected chromosomes 1, 5, 9 and 11 and the corresponding differential methylation profiles determined by competitive hybridization of AUMA products from normal and tumor tissue to BAC arrays are shown. Positive values (to the right) indicate hypomethylations, negative values (to the left) indicate hypermethylations.
Resultats 114
U M A B Ao1 63 N
Aq3 63 T
Ar3 74 N
Ap1 74 T
Au4 HT29
As3 HCT116
Supplemental Figure 6
Direct sequencing of Bisulfite treated DNAs. (A) Confirmation of the methylation change seen in AUMA fingerprints of 6 different bands. All bands except Ao1 and Aq3 have been sequenced with forward primers. Raw data electropherograms for bands Ao1 and As3 are shown. (B) Sequences corresponding to Aq3 hypomethylated band. A completely demethylated tumor (63T), a partially demethylated tumor (74T) and a demethylated cell line (HT29) are shown. All normal tissues and HCT116 cell line are heavily methylated at all CpGs. The sequence shown corresponds to a fragment of the Alu Y element. All sequences were obtained with reverse primer.
Resultats 115
A
N1
T1 Supplemental Figure 7
Dissociation curves of bisulfite treated DNAs amplified by real time PCR. Results corresponding to three normal (N)-tumor (T) pairs are illustrated. Methylated sequences T2 N2 exhibit a higher melting temperature than unmethylated sequences due to a higher CG content. (A) Analysis of the AluSx sequence represented by Aq3 AUMA band. In all normal cases the sequence is methylated. Tumor 1 is methylated, tumor 2 is partially methylated, and tumor 3 is fully unmehylated.
N3 T3
Resultats 117
CAPÍTOL 3
Bivalent domains enforce transcriptional memory of DNA methylated genes in colorectal cancer.
Jairo Rodríguez, Mar Muñoz, Laura Vives, Gus Frangou, Mark Groudine, Miguel A. Peinado.
Article en preparació.
Resultats 118
Bivalent domains enforce transcriptional memory of DNA methylated genes in colorectal cancer.
El DNA es troba empaquetat en el nucli eucariòtic en forma d‟una nucleoproteïna anomenada cromatina, la qual és el substrat de la regulació epigenètica. Així doncs, de l‟estructura de la cromatina en depenen processos tant importants com la replicació, recombinació i transcripció, a més a més d‟afectar el grau d‟empaquetament dels cromosomes. L‟alteració dels mecanismes de regulació de la cromatina in vivo comporten l‟aparició de processos com el càncer.
La regulació epigenètica es dóna a dos nivells: la metilació del DNA i les modificacions de les histones, i totes dues tenen efectes sobre l‟estructura de la cromatina, a través del posicionament dels nucleosomes, les unitats bàsiques de la cromatina. L‟estudi dels patrons de metilació en càncer indiquen que la metilació del DNA en regions promotores és un mecanisme molt freqüent d‟inactivació gènica en tumors, la qual va associada a patrons de modificacions d‟histones de cromatina inactiva.
Els darrers estudis globals sobre la distribució genòmica de modificacions d‟histones en càncer indiquen que els gens silenciats amb promotors hipermetilats presenten uns patrons de cromatina específics sobre els seus promotors molt similars als descrits en cèl·lules mare embrionàries (ESCs), els dominis bivalents, en els quals conviuen dues marques tradicionalment relacionades amb estats transcripcionals oposats, la trimetilació de la histona H3 en la lisina 4 (H3K4me3) i 27 (H3K27me3). Aquests resultats aporten noves evidències moleculars a un hipotètic origen stem del càncer.
En aquest treball hem aprofundit en l‟estudi dels patrons epigenètics aberrants en càncer colorectal (CCR), amb una especial atenció sobre el silenciament transcripcional associat a la hipermetilació de promotors. Així hem pogut identificar un nou conjunt de gens veïns hipermetilats i silenciats en CCR en la regió genòmica 5q35.2, els quals posseeixen promotors amb dominis bivalents. Aquest fet evidencia l‟existència d‟una relació entre les maquinàries de metilació del DNA i de regulació de la memòria cel·lular en ESCs i cèl·lules diferenciades a través dels complexes que regulen els nivells de H3K4me3 i H3K27me3, les proteïnes Trithorax i Polycomb, respectivament.
De forma inesperada, els resultats obtinguts demostren que la reexpressió per mitjà de drogues desmetilants del DNA i inhibidors de HDACs dels gens silenciats no aconsegueix esborrar els dominis bivalents, fet que posa de manifest l‟existència de mecanismes de memòria cel·lular que mantenen l‟estat transcripcional en les cèl·lules tumorals i molt probablement en les cèl·lules normals. Contràriament, gens que s‟expressaven a un alt nivell en el teixit normal passen a infraexpressar-se després del tractament amb les drogues, adquirint modificacions d‟histones pròpies d‟un estat inactiu.
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Bivalent domains enforce transcriptional memory of DNA methylated genes in colorectal cancer.
Jairo Rodriguez1,2, Mar Muñoz2, Laura Vives2, Gus Frangou3, Mark Groudine3, Miguel A. Peinado1,2
1 Institut d‟Investigació Biomèdica de Bellvitge (IDIBELL), L‟Hospitalet, Barcelona, Spain 2 Institut de Medicina Predictiva i Personalitzada del Càncer (IMPPC), Badalona, Barcelona, Spain 3 Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
Running title: Bivalent domains enforce transcriptional memory
Keywords: DNA methylation, transcriptional silence, bivalent domains, demethylating agents, cancer.
Correspondence: Miguel A. Peinado Institut de Medicina Predictiva i Personalitzada del Càncer (IMPPC) Crta Can Ruti, Camí de les Escoles, Badalona, 08916 Barcelona, Spain email: [email protected] Tel 34-93 4978694 Fax 34-93 4978697
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Abstract
There is little doubt that epigenetic abnormalities lay at the foundation of carcinogenesis, providing an alternative to genetic disruption for almost all defined pathways. Although the list of silenced genes with promoter DNA methylation is long and still growing, no answer is given as to why some genes become DNA-methylated during carcinogenesis. Recent works reporting on genome-wide maps of histone modifications have provided some clues, indicating that stem- cell chromatin signatures are found over the promoters of genes that will become methylated in adult cancers. Here we report a novel set of neighboring DNA methylated genes in a region spanning 1.2 Mb in colorectal carcinoma cells, where promoter DNA methylation occurs only at genes that are kept in a low transcriptional state by the presence of bivalent domains. Upon drug-induced demethylation, silenced genes become upregulated but bivalent domains do not resolve into an active chromatin conformation. Furthermore, active genes in the region become downregulated after drug treatment, accompanied by a de novo formation of bivalent domains. Our results demonstrate that bivalent domains mark the promoters of genes that will become DNA methylated in tumor cells, enforcing transcriptional silence and thus connecting stem cell biology and tumor development.
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Introduction
During decades, scientists have focused on the understanding of the origins of cancer, trying to dissect the sequence of events that deregulate the complex networks governing homeostasis in multicellular organisms. Cancer is a disease that ultimately alters gene expression. In this context, even though we have more precise maps of the human genome1, this has proved insufficient to understand how genetic programs are read and translated into transcriptional patterns in normal and pathological cells. A large body of data indicates that information other than the encoded within the DNA sequence is required. This “other” information, termed epigenetic (which literally means “over genetic”), is defined as the heritable changes that affect gene expression without altering the DNA sequence2. To date there is little doubt that epigenetic events, in an intimate cooperation with genetic events, are involved in every step of tumorigenesis. Moreover, for all the key cellular pathways disrupted in human cancers, epigenetic alterations provide an alternative mechanism to genetic inactivation of tumor suppressor genes (Reviewed in3-5) and, for a growing number of genes, epigenetic inactivation represents the only inactivating mechanism3,4.
Epigenetic information is encoded as a heritable combination of chemical modifications to both DNA and its packaging histones (Reviewed in6,7). Methylation of the cytosine base within the CpG dinucleotide is the main epigenetic modification of the DNA (Reviewed in2,8). Much of the human genome is CpG depleted, but this dinucleotide can be found at close to its expected frequency in small genomic regions (200bp to a few kb), known as CpG islands9,10. These areas are usually “protected” from methylation and are located in the proximal promoter regions of 75% of human genes2,8,11. Methylated CpG islands are strongly and hereditably repressed8 and therefore, DNA methylation has been considered as a mark of long-term inactivation8,12,13.
Abnormal DNA methylation patterns are a common hallmark of all human cancers (Reviewed in14). Even though widespread genomic hypomethylation was the first DNA methylation abnormality detected in tumors (Reviewed in15,16), most investigations have focused on the study of abnormal de novo methylation of CpG islands and its association with the transcriptional silencing of many cancer-related genes3,4,14,15,17-19. To date, little is known about the mechanisms that bring de novo methylation to the promoter regions of genes that are usually methylation-free, during carcinogenesis. In the above context, chromatin, and more importantly, histones and their modifications, are revealed as a key component of the silencing machinery that may have a profound impact on the DNA methylation patterns. Silenced genes, including those with promoter DNA methylation, have been found to contain a specific code of histone modifications across the promoter region and within the body of the gene, that are thought to be a signature of the sequence of silencing events that lead to transcriptional inactivation (Reviewed in20). Additionally, an extra layer of complexity is added by the fact that the DNA methylation mark itself can be read by specific proteins that are able to ultimately recruit activities that alter chromatin composition (Reviewed in8). This highlights the notion that a crosstalk, but not a unidirectional flow of information, exists between DNA methylation and histone modifications to orchestrate transcriptional silencing.
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In recent years, a very exciting concept has emerged which hypothesizes the existence of cancer stem cells, which would be responsible for perpetuating the tumor population (Reviewed in5). A growing number of evidences suggest that disruption in cancer cells of the two main cell memory systems involved in the maintenance of a stem cell state, Trithorax (Trx) and Polycomb Group (PcG) proteins may be involved in tumor-associated aberrant gene silencing and promoter DNA methylation. In this context, epigenetic stem cell-like signatures have been found in a large set of DNA-methylated genes in different types of tumors: trimethylated lysine 27 has been found to pre-mark genes in colorectal and prostate cancer cells21; methylated lysine 27 and components of the Polycomb Repressor Complex 2 (PRC2) responsible for the deposition of this mark, SUZ12 and EED, have been found over the promoters of a different subset of genes in primary colorectal tumors22; and the activation mark dimethyl lysine 4 in histone H3 together with methylated lysine 27 have been found to coexist over the promoter regions of DNA methylated genes in embryonic carcinoma (EC) cells23, in an epigenetic landscape that mimics the bivalent domains described by Bernstein and colleagues for a subset of key developmentally regulated genes in embryonic stem (ES) cells24.
The above scenario suggests that the aberrant de novo DNA methylation that so commonly affects cancer-related genes, could be a direct consequence of an aberrant chromatin environment, driven by the abnormal presence of the polycomb-mediated mark methylated lysine 27. Further evidence supporting this hypothesis comes from experiments conducted by Vire and collaborators25, revealing that the histone methyltransferase responsible for the deposition of the methyl mark over lysine 27, Enhancer of Zeste Homolog 2 (EZH2), which forms part of the Polycomb Repressor Complex 2 (PRC2)(Reviewed in26), physically interacts with DNMT activities to bring de novo DNA methylation over the promoters of polycomb regulated genes. However, recent experiments by McGarvey and colleagues27 show that only lightly DNA-methylated cancer-related genes become upregulated upon EZH2 knockdown, while densely DNA-methylated genes remain unaffected in terms of DNA methylation and transcriptional activity. Consequently, further experiments must be performed to ascertain the possible causes of aberrant de novo DNA methylation in cancer.
Global approaches that couple the use of antibodies against methylated cytosine to microarray analysis have helped to better understand how abnormal methylation patterns are established during tumor development, bringing to light some unveiled features. It has been recently shown that de novo methylated genes in the colorectal cancer cell line CaCo2, which fall in a few functional categories, tend to cluster together in the genome, suggesting the existence of an instructive de novo methylation mechanism directing DNA methylation to specific set of closely related genes28. In agreement with these observations, two genomic regions where DNA methylation encompasses large portions of the cancer cell genome, affecting multiple neighboring CpG island-containing genes, have been described in a cancer system29,30. Similarly, other regions have been described where silencing of multiple neighboring genes in large genomic regions occur in the absence of DNA methylation31. Taken together, these experiments highlight the necessity of exploring the cancer cell epigenome to search for clusters of DNA-methylated genes in order to gain insights into how aberrant epigenetic landscapes are set during tumorigenesis.
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Here, we report the characterization of the region around a novel DNA-methylated gene, the CPLX2 locus, showing a novel set of neighboring genes that undergo frequent promoter DNA hypermethylation in human primary colorectal tumors and cell lines. The genes, which include hormone receptors, cell-to-cell adhesion and signaling molecules and tissue specific genes, are found in a genomic region spanning over 1.2 Mb mapping in 5q35.2. Chromatin analysis has revealed that DNA methylated genes are kept in a silent state by the presence of a stem cell- like chromatin pattern defined by the simultaneous presence of the trimethylated forms of lysines 4 and 27 in histone H3 and hypoacetylated forms of histones H3 and H4. We further demonstrate the direct implication of the polycomb repressor complex 4 in the silencing of this region.
Reactivation of the silenced genes with the demethylating agent 5azaC and the HDAC inhibitor TSA results in a striking pattern of histone modifications across the entire region, indicating that drug treatment is not able to erase chromatin patterns at DNA-methylated promoters, but rather forces the cell to retain the bivalent domains by adjusting the levels of both lysine 4 and 27 trimethylation.
Our results show for the first time the concurrent DNA methylation of neighboring tumor- associated genes across a large genomic region, which are embedded in a stem cell-like chromatin pattern where DNA methylation locks in a silent state. These experiments, in concordance with recent reports, directly point to a direct involvement of the two main cell- memory systems, polycomb and trithorax, in the aberrant promoter DNA methylation that so commonly affects silenced genes in cancer.
Materials and methods
Tissues and cell lines A series of 121 colorectal tumors (113 carcinomas and 8 adenomas) and their paired areas of normal colonic mucosa was used in the analysis. Samples were collected simultaneously as fresh specimens and snap-frozen within 2 h of removal and then stored at -80ºC. All samples were obtained from the Ciutat Sanitària i Universitària de Bellvitge (Barcelona, Spain). Transformed cell content was higher than 75% in most tumor specimens as assessed by histological examination. The study protocol was approved by the Ethics Committee. Human colon cancer cell lines (HT29, SW480, HCT116, LoVo, DLD-1, CaCo-2 and LS174T) were obtained from the American Type Culture Collection (ATCC; Manassas, VA). The KM12C and KM12SM cell lines were generously provided by A. Fabra. DNA from tumor-normal pairs was obtained by conventional organic extraction and ethanol precipitation. DNA purity and quality was checked in 0.8% agarose gel electrophoresis. RNA from cell lines was obtained by phenol- chloroform extraction and ethanol precipitation following standard procedures.
Amplification of Unmethylated Alu (AUMA) The genomic region methylated in colorectal tumors and cell lines described here was identified as a single band undergoing recurrent hipermethylation in DNA methylation fingerprints (Figure
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1A), obtained by the application of a novel DNA methylation screening method named Amplification of Unmethylated Alu (AUMA)32. Briefly, one microgram of DNA was digested with 20 U of the methylation sensitive restriction endonuclease SmaI (Roche Diagnostics GmbH, Mannheim, Germany) for 16h at 30ºC, leaving cleaved fragments with blunt ends (CCC/GGG). Adaptors were prepared incubating the oligonucleotides Blue (CCGAATTCGCAAAGCTCTGA) and the 5‟ phosphorilated MCF oligonucleotide (TCAGAGCTTTGCGAAT) at 65ºC for 2 min, and then cooling to room temperature for 30-60 min. One microgram of the digested DNA was ligated to 2nmol of adaptor using T4 DNA ligase (New England Biolabs, Beverly, MA). The products were purified using the GFX Kit (Amersham Biosciences, Buckinghamshire, UK) and eluted in 250 μl of sterile water.
Subsequent PCR amplification using a single primer named BAuTT (ATTCGCAAAGCTCTGAGGGTT) allowed the generation of fingerprints that were resolved on denaturing sequencing gels. Radioactive AUMA‟s were performed for normal-tumor comparisons, yielding a highly reproducible and clear band patterning that allowed a feasible identification of specific bands undergoing tumor-associated hypermethylation (bands with a decreased intensity in the tumor compared to the normal tissue) and tumor-associated hypomethylation (bands de novo appearing or with an increased intensity in the tumor, compared to the normal tissue).
Bisulfite genomic sequencing The bisulfite reaction was carried out on 2µg of mechanically sheared DNA for 16h at 55˚ under conditions previously described29. Prior to sequencing, DNA was amplified using a nested or semi-nested PCR approach, as appropriate. A minimum of two independent PCRs were carried out and pooled together to ensure a representative sequencing. Alternatively, DNA methylation was assessed by real-time PCR melting curve analysis. Positions and sequence composition of the analyzed CpG islands are listed in Supplementary Table 1 and bisulfite genomic sequencing PCR primers are listed in Supplementary Table 2.
Gene expression analysis Whole cDNA was obtained by retrotranscription of 500 ng of whole RNA with M-MLV retrotanscriptase (Invitrogen, Carlsbad, CA) using random hexamers (Amersham Biosciences) at 37ºC for 1h. cDNA levels were quantified using the LightCycler 2.0 real time PCR system with Fast Start Master SYBR Green I kit (Roche Diagnostics). For a 10 μl PCR reaction volume, 1 μl of cDNA and 9 μl of mastermix were added to each capillary. Mastermix was prepared to a final concentration of 3.5 mM MgCl2 and 1 μM of each primer. Primers used for expression analysis are listed in Supplementary Table 3.
Chromatin Immunoprecipitation (ChIP) assay Briefly, 6x106 cells were washed twice with PBS and cross-linked on the culture plate for 15 min at room temperature in the presence of 0.5% formaldehyde. Cross linking reaction was stopped by adding 0.125M glycine. All subsequent steps were carried out at 4˚C. All buffers were pre- chilled and contained protease inhibitors (Complete Mini, Roche). Cells were washed twice with PBS and then scraped. Collected pellets were dissolved in 1ml lysis buffer (1% SDS, 5mM EDTA,
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50mM Tris pH8) and were sonicated in a cold ethanol bath for 10 cycles at 100% amplitude using a UP50H sonicator (Hielscher, Teltow, Germany). Chromatin fragmentation was visualized in 1% agarose gel. Obtained fragments were in the 200 to 500 bp range. Soluble chromatin was obtained by centrifuging the sonicated samples at 14.000g for 10 min at 4˚C. The soluble fraction was diluted 1/10 in dilution buffer (1% Triton X-100, 2mM EDTA, 20mM Tris pH8, 150mM NaCl) then aliquoted and stored at -80˚C until use.
Immunoprecipitation was carried out at 4˚C by adding 5 to 10 µg of the desired antibody to 1ml of chromatin. Soluble chromatin was immunoprecipitated with specific antibodies against BmiI, acetylated (Ac) H3K9, Ac H3K14, Ac H4K12, Ac H4K16, dimethylated (me2) H3K9me2, trimethylated (me3) H3K9me3, H3K27me3 (Upstate, Millipore, Billerica, MA), H3K4me3 (Abcam, Cambridge, UK), EZH2 (Santa Cruz, Santa Cruz, CA), SirT1 (Delta Biolabs, Gilroy, CA) and IgG negative control (Jackson Immunoresearch, West Grove, PA). Chromatin-antibody complexes were pulled down using a 50% slurry protein A/G (Upstate) and subsequently washed and eluted according to the manufacturer‟s instructions. Eluted samples were cleaned using the Jet quick PCR product purification spin kit (Genomed, Löhne, Germany).
Enrichment for a given chromatin modification was quantified as a fold enrichment over the input using quantitative real time PCR platform (Light Cycler 2.0, Roche). The input value was obtained for each amplification reaction and sample as the mean value of the 1/10 and 1/50 dilutions. Amplification efficiency and linearity were assessed by using serially-diluted samples that allowed the generation of a standard curve for every PCR. All quantifications were performed in duplicate. Primers used for Real Time PCR ChIP analysis are listed in Supplementary Table 4.
Drug treatments Briefly, HCT116 cell line was seeded at low density 24h prior to treatment. 5-aza-2‟- deoxicytidine (5azaC) was added to media for 48h, after which drug was removed and cells were allowed to recover in fresh media for further 24h before harvesting DNA and RNA. For 5azaC and Trichostatin A (TSA) co-treatments, cells were grown in the presence of 5azaC for 48h after which TSA was added for further 18h. Before harvesting RNA and DNA, fresh media was added for further 24h.
Results
Discovery of a new DNA-methylated gene in colorectal tumors By applying AUMA to a series of colorectal tumors and their paired normal mucosas32 (unpublished results), we identified a specific band, termed Ap1 (Figure 1A), which was found extensively methylated in 36 out of 50 (72%) tumor samples. Gel isolation, cloning and sequencing, allowed us to map the Ap1 band on the genomic region 5q35.2. As expected, the Ap1 band contained the two SmaI sites on both ends. The SmaI site on the 3‟end of the band was inside of a MIR interspersed repeat (Figure 1B). Direct sequencing of bisulfite treated DNAs showed that this MIR repeat is devoid of methylation in normal samples but partially to
Resultats 126 completely methylated in tumor samples and colorectal cancer cell lines (Figure 1C). The 5‟ end of the Ap1 band mapped inside of the first intron of the Complexin 2 (CPLX2) gene (Figure 1B). Since this SmaI site is only 31 bp away from the CPLX2 CpG island (CpG125), we assessed its methylation status by bisulfite sequencing, showing that this normally unmethylated CpG island can be found partially to heavily methylated in colorectal tumors and cell lines (Figure 1C).
Methylation around the CPLX2 locus encompasses multiple CpG islands As depicted in figure 2A, the genomic region containing the CPLX2 gene is moderately rich in genes and CpG islands. Given that previous works had reported that large genomic regions may undergo concurrent methylation of multiple CpG islands in tumors29, we further extended the methylation analysis to genes upstream and downstream the CPLX2 locus. In a first setting, CPLX2 locus neighboring CpG islands were analyzed for DNA methylation by bisulfite sequencing in five normal-tumor pairs and 8 colorectal cell lines (figure 2B). The first island upstream of the CPLX2 gene (CpG72) is located 137.8 Kb away and is associated to the Histamine Receptor H2 (HRH2) gene, while the first island downstream (CpG125) is situated 73.8 Kb away and is associated to a transcriptional variant of the CPLX2 gene named AK124.837 (Figure 2A), which has been reported to be transcriptionally controlled from an alternative promoter33.
In a similar way to CPLX2 CpG island (CpG118), the HRH2 CpG island (CpG78) was found to be completely devoid of methylation in 4 normal tissues (we detected low methylation in one normal tissue), while moderate methylation levels were detected for the same samples in the CpG125 island. Both islands were found heavily methylated in all tumors and cell lines (CpG72) and in 6 of 7 cell lines and 4 of 5 tumors (CpG125). The next two islands to be tested were CpG93, associated to the Sideroflexin 1 (SFXN1) and CpG52 associated to the Tho Complex 3 (THOC3), laying 317.5 Kb upstream and 170.2 Kb downstream of the CPLX2 CpG island respectively. Both were the first islands to be found completely methylation-free in all normal- tumor pairs and cell lines. The last two islands to show methylation around the CPLX2 gene were CpG145 associated to the Dopamine Receptor D1 (DRD1), laying 351.2 Kb upstream, and the CpG78, located inside of a segmental duplication, mapping 262.8 Kb downstream. In the case of the DRD1 CpG island, it was found methylation-free in all normal tissues, while it was methylated in all cell lines and it was only found heavily methylated in one tumor sample and partially methylated in two other tumors. On the contrary, CpG78 contained low levels of DNA methylation in all normal tissues, turning to medium methylation in one tumor sample and heavy methylation in the other four tumors and all cell lines. The region from the DRD1 gene to the CpG78 (the two most distal ends of the methylated set of genes around CPLX2) spanned so far 617.6 Kb.
More genes were included in the analysis downstream of the methylated CpG78 (see figure 2A). A second DNA-methylated region was found affecting CpG91, CpG114 and CpG79. CpG91 is a CpG island associated to the 3‟ ends of the Protocadherin Lung, Kidney and Colon gene (PcLKC) and the G protein-regulated inducer of neurite outgrowth (GPRIN1). It was found to be non- methylated in all normal-tumor pairs except for one tumor tissue were low methylation levels were detected. Four of the 7 cell lines displayed heavy methylation in the same island. PcLKC
Resultats 127 has no CpG island associated to its proximal promoter region (Figure 2A). CpG114 is associated to the GPRIN1 promoter and was found partially methylated in 4 of 5 normal tissues, showing increased methylation levels in 3 tumors. Cell lines exhibited again higher methylation levels, with 5 cell lines showing heavy methylation, one showing medium methylation and only one cell line showed no methylation. Finally, CpG79 associated to the β-synuclein gene was methylation free in 4 of 5 normal tissues (one normal sample exhibited low methylation levels) while 3 tumors displayed medium methylation and 2 tumors showed heavy methylation. Three of the cell lines displayed heavy methylation, two displayed medium methylation levels and two cell lines were methylation-free. The rest of the CpG islands analyzed showed to be methylation free in all normal-tumor pairs and all cell lines. These include CpG47, CpG100 associated to the Clathrin B (CLTB) gene, CpG126 associated to the Ring Finger 44 (RNF44) gene and CpG47 associated to the Tetraspanin 17 (TSPAN17) gene. Summarizing all the above mentioned, we have discovered a novel set of DNA-methylated, tumor-associated neighboring genes, that lay in two different subregions, for which the most distal ends, DRD1 and SNCB, map 1203.8 Kb away.
Methylation of multiple genes in 5q35.2 is a common event in colorectal carcinogenesis We have determined the methylation status of the most heavily methylated genes HRH2, CPLX2, AK124.837 and SNCB genes over a series of 121 colorectal normal-tumor pairs, which included both adenomas and carcinomas. Real-time PCR melting curve analysis was the method used here for a high throughput analysis (see materials and methods) (Figure 3A). In those cases were methylation status was not clear, direct bisulfite sequencing was performed. In concordance with direct bisulfite sequencing data, the four analyzed genes were found extensively methylated in all tumor samples, including adenomas, being HRH2 the most frequently methylated (72.7%), followed by CPLX2 (70.8%), SNCB (68.6%) and AK124.837 (63%)(Figure 3C).
Methylation of any of the four genes affected virtually all tumors (119 out of 121) (Figures 3B and 3C). Among carcinomas, few cases exhibited methylation of only 1 gene (8.1%), while this figure was 26.3% for two methylated genes, 36.3% for three methylated genes and 28.1% for four methylated genes (Figure 3B). Adenomas showed a slightly different distribution, with most samples displaying 1 (25%) or 2 (27%) methylated genes and a lower percentage (12.5%) showing methylation of three or four genes (Figure 3B). Even though the genes described in this region tend to be methylated concurrently, a methylation pattern could not be discerned, therefore indicating that there is not a unique order in the methylation of the genes in this region (Figure 3C).
Transcriptional silence across genes in 5q35.2 Expression profiles of a total of 12 genes (including those genes with a methylated CpG island and a subset of those displaying no-methylation) were investigated for a subset of 16 normal tumor pairs. As illustrated in Figure 4A, DNA-methylated genes are almost invariably downregulated, with the exception of CPLX2, which is methylated and moderately upregulated in tumor sample 10655, HRH2 and SNCB which are methylated and upregulated in tumor
Resultats 128 sample 10659 and HRH2, which is methylated and upregulated in tumor sample 10886 (Figure 4A). Interestingly, transcriptional silencing also affects genes that are non-methylated. Among these genes, there are those that have been found methylated in tumors and cell lines (DRD1, HRH2, CPLX2, AK124.837, GPRIN1 and SNCB), genes in which DNA methylation has never been found (SFXN1, CLTB, RNF44 and TSPAN17) and genes without CpG island (PcLKC). An extreme example of this situation is tumor sample 10845, which in spite of displaying no methylation at any promoter CpG island, shows downregulation of 10 of the 12 genes analyzed (Figure 4A).
Mean expression ratios (Figure 4B) and mean absolute expression values (Figure 4D) for the 16 normal-tumor pairs clearly indicate that most of the genes in 5q35.2 undergo transcriptional downregulation in tumors, with the exception of THOC3 and RNF44 genes, which tend to be moderately upregulated. Nevertheless, the different genes in the region do not become downregulated to the same extent, being AK124.837, PcLKC and SNCB the most downregulated ones. With some exceptions, colon cancer cell lines also exhibited methylation and silencing of the same genes affected in tumors. SFXN1, which is upregulated in cell lines and downregulated in primary tumors, was the main difference (Figure 4C-E). A perfect association between promoter DNA methylation and transcriptional silencing was observed in HCT116 cells (Figure 4C) and the rest of cell lines (data not shown). SW480 cell line displays a characteristic profile of the region. Specifically, CPLX2 and AK124.837 escape transcriptional silencing in accordance with the unmethylated status of their CpG islands, higher expression levels for unmethylated genes such as THOC3, RNF44 and SNCB and very high levels for the unmethylated genes CLTB, GPRIN1 and TSPAN17 (Figure 4E).
As a whole, expression profiles of this region in normal and tumor cells suggests the existence of interspersed regions containing highly transcribed genes (SFXN1, genes from THOC3 to RNF44 and TSPAN17), with other regions containing low-expression genes (DRD1, genes from HRH2 to AK124.837 and genes from PcLKC to SNCB).
Chromatin profiles identify different chromatin domains from DRD1 to TSPAN17 In order to gain insights into the mechanisms that lead to transcriptional silencing in tumors we profiled active and inactive histone marks along the region from DRD1 to TSPAN17 (Figure 5). Chromatin profiles were performed using the chromosomically stable HCT116 cell line. The close similarity between the expression and methylation profiles displayed by HCT116 cell line and primary tumors makes this cell line a good model into which perform this kind of experiments.
A panel of 17 primer sets were used in the analysis (Supplementary table 4), covering promoter regions (all genes) and CpG islands and intronic regions of the three most DNA methylated and downregulated genes (HRH2, CPLX2 and SNCB). As summarized in figure 5B, the “active” mark acetylated lysine 9 on histone H3 (Ac H3K9) is highly enriched over the promoter regions of the most highly transcribed genes (Figure 5A), defining three peaks of acetylation in SFXN1, RNF44 and TSPAN17. Hypoacetylation pockets included the DRD1 gene, the region spanning from HRH2 to AK124.837, and the region including PcLKC and SNCB. Noteworthy, all these genes appeared hypermethylated in cancer cells. Interestingly, acetylation of the Lysine 14 in histone
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H3 (Ac H3K14) was already loaded in all regions tested, not showing any correlation with the active or silent genes (Supplementary figure 1). Nevertheless, the different genes showed slight differences in the content of this mark, with the highest amounts detected in the HRH2 promoter, CPLX2 intronic region, PcLKC promoter and SNCB promoter and intronic region, suggesting a weak correlation between this mark and inactive genes. Acetylated lysine 16 in histone H4 (Ac H4K16) was globally depleted in the region, except for the highly expressed RNF44 and the CpG island-free PcLKC gene. Very low levels of acetylated lysine 12 of the histone H3 was detected (see supplementary figure 1). The last “active” mark profiled was the trimethylated form of the lysine 4 in histone H3 (H3K4me3). This mark was found to be present in moderate amounts in all regions tested, with the highest enrichment over the actively transcribed, methylation-free SFXN1 and RNF44 genes and in the promoter CpG islands of the DNA methylated genes HRH2 and CPLX2 (Figure 5B).
With regards to histone modifications associated to inactive states, we surveyed for the dimethylated and trimethylated forms of the lysine 9 in the histone H3 (H3K9me2 and H3K9me3, respectively) and the trimethylated lysine 27 in the histone H3 (H3K27me3). Surprisingly, while no significant levels of H3K9me2/me3 could be found at any of the regions tested, including intronic regions of silenced genes HRH2, CPLX2 and SNCB (see supplementary figure 1) high levels of H3K27me3 were found mainly over the CpG islands of the DNA methylated genes DRD1, HRH2, CPLX2, AK124.837 and the DNA unmethylated but downregulated SNCB gene (Figure 5B). These are the same genes that showed depletion for Ac H3K9 and Ac H4K16 and moderate levels of Ac H3K14 and H3K4me3. Conversely, low to undetectable levels of the H3K27me3 mark were found over the promoters of the DNA unmethylated, active genes SFXN1, THOC3, RNF44 and TSPAN17. Even though CLTB and PcLKC were found downregulated in the HCT116 cell line, they showed very low levels of H3K27me3 over their promoter regions.
Demethylating agents derepress silent genes and affect the expression of active genes across 5q35.2 Re-expression patterns were assessed using HCT116 cell line for genes spanning from DRD1 to TSPAN17 using a pharmacological approach with the demethylating agent 5azaC and the class I and II HDAC inhibitor TSA, both alone and in combination. Genes could be grouped into four different groups according to their behavior after drug treatment (Figure 6). The first group of genes, corresponding to the two DNA-methylated genes HRH2, CPLX2, the unmethylated SNCB and the CpG island-free PcLKC were upregulated to different levels upon 5azaC treatment alone. Only the CPLX2 gene showed a synergistic re-expression after 5azaC and TSA co- treatments, while the rest of the genes in this group showed weak (HRH2 and SNCB) or no (PcLKC) upregulation of the co-treatment over the 5azaC treatment alone. A second group, including the DNA-methylated genes DRD1 and AK124.837, were only moderately upregulated after drug co-treatment, remaining unaffected after 5azaC treatment alone. The third group of genes, CLTB and TSPAN17 (both DNA-methylation free) were unaffected by drug treatment, either 5azaC and TSA alone or in combination. Finally, a fourth group included the highly- expressed, DNA metylation-free genes SFXN1, THOC3 and RNF44. Contrary to the rest of the genes in the region, these genes became moderately downregulated after drug treatment and
Resultats 130 co-treatment. 5azaC and 5azaC/TSA co-treatments dropped SFXN1 expression levels to 57% and 63% respectively, while these figures were 71% and 46% for THOC3 and 64% and 17% for RNF44 (Figure 6). TSA treatment alone was unable to upregulate genes with methylated promoters, while downregulated genes displaying unmethylated promoters were weakly upregulated upon TSA treatment alone (see supplementary figure 3).
Chromatin modifications of drug-induced, re-expressed genes. We have described how drug treatment differentially affects gene expression across 5q35.2, depending on gene basal expression level and DNA methylation status. Thus, we wondered how the histone modification landscape was affected by the drug treatment. Chromatin state in 17 regions was characterized in the untreated HCT116 cell line and compared with cells treated with 5azaC and 5azaC/TSA (Figure 7). A two to four fold enrichment for the Ac H3K9 mark was seen in all genes tested after 5aza/TSA co-treatment, being the promoters and CpG island regions of silenced genes (DRD1, HRH2, CPLX2 and SNCB) the areas showing the highest enrichment, plus the THOC3 gene, which also displayed a moderate enrichment for this modification. Interestingly, the enrichment was more clearly seen over CpG islands than over the promoter and intronic regions of DNA-methylated genes, as is the case of HRH2, CPLX2 and SNCB (Figure 7). On the other hand, only DNA-methylated genes displayed a low enrichment for this active mark after 5azaC treatment alone (promoter region and CpG island of HRH2 and SNCB genes and CpG island of CPLX2 gene), while the rest of the genes displayed a mild decrease for the same modification (Figure 7).
An unexpected result was obtained regarding Ac H4K16 modification. Treatment with 5azaC alone induced a low to moderate erasure of this mark at virtually all regions analyzed. On the contrary, drug co-treatment had the opposite effect, highly enriching this modification over HRH2, CPLX2 and SNCB, having a moderate enrichment over DRD1 and CLTB and a low effect over PcLKC and TSPAN17 (Figure 7). Highly expressing genes showed no enrichment for this modification. This is of special relevance given the fact that the HDAC is supposed to deacetylate this aminoacid residue, the NAD+ dependent class III HDAC SirT1, has been previously involved in the silencing of other DNA methylated tumor suppressor genes34. Interestingly, the present data suggests an active role of SirT1 in the maintenance of the repressed state, independently of promoter DNA methylation.
Only silenced genes, DNA methylated or not, were enriched for H3K4me3 after both 5azaC and combined drug treatment, while highly expressing genes had their H3K4me3 levels depleted after the treatments (Figure 7). Globally, H3K4me3 is the only mark that showed a clear enrichment over the promoters of silenced genes after both 5azaC treatment alone and co- treatment. The highest enrichments were detected once again around the promoter regions of the DNA-methylated genes DRD1, HRH2, CPLX2, AK124.837 and SNCB (Figure 7). Moderate enrichments were seen over CLTB and PcLKC. On the other hand, H3K4me3 levels were reduced after 5azaC treatment alone at the SFXN1 promoter, while depletion for this mark was seen only after drug co-treatment at the THOC3 and TSPAN17 promoters. Only RNF44 showed reduced levels for this mark after both 5azaC treatment and co-treatment with TSA.
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Finally, and most surprisingly, while H3K27me3 levels were weakly affected by 5azaC treatment alone, co-treatment with TSA clearly induced a global gain of this histone modification across the entire region (Figure 7). Enrichment for this mark affected all genes, including active genes, which had been previously characterized to have very low or undetectable levels of this mark. These results clearly indicate that this classic inactive histone modification not only remains after 5azaC treatment but increases with transcriptional upregulation upon drug co-treatment.
Components of polycomb repressor complexes are found at the inactive genes. The expression profiles and histone modification patterns before and after drug treatment clearly suggest that a specific histone code exists around the promoters of silenced genes, especially over those with promoter DNA methylation. The specific presence of H3K27me3 indicates that components of the polycomb group of proteins may be mediating, at least in part, the silencing that affects most of the genes across this region. We therefore attempted to identify the polycomb proteins EZH2 and BmiI, over the promoter regions of the silenced genes. Additionally, specific enrichment of Ac H4K16 over the promoters of silenced genes predicted the presence of the HDAC SirT1, which was also included in the analysis. As expected, the histone methyltransferase responsible for the deposition of the methylation mark over lysine 27, EZH2, was found enriched over the promoters of the silenced genes where we had previously detected enrichment for its methylation mark, H3K27me3 (Figure 8). This was consistent with the presence, over the same regions of the polycomb member BmiI, which forms part of the polycomb repressor complex 1 (PRC1) that mediates recognition of the H3K27me3 mark, and the HDAC SirT1.
Drug treatment resulted in an increase of the EZH2 protein over the promoter regions of all genes, which provides a plausible explanation for the global increase of the H3K27me3 mark upon reactivation. BmiI was consistently depleted in all genes after reactivation. Finally, while 5azaC treatment alone increased the levels of SirT1 at some promoters (HRH2, CPLX2, AK124.837 and CLTB), drug co-treatment tended to reduce the levels of this HDAC at all promoters tested (Figure 8).
Discussion
Concurrent methylation of multiple CpG islands across 5q35.2 is a common event in colorectal carcinogenesis
Aberrant de novo DNA methylation is the most well characterized epigenetic alteration in carcinogenesis and is associated with the transcriptional silencing of a large list of genes within multiple pathways relevant to tumor biology5,14. Even though multiple hypermethylation events can be found affecting a single tumor cell, hypermethylation has been always considered to be restricted to individual, discrete CpG island-containing genes. This paradigm has changed since the discovery that promoter DNA methylation and chromatin remodeling can also encompass several megabases, as has been recently described in colorectal29,30 and bladder cancers31. By applying a DNA methylation screening methodology that specifically targets unmethylated
Resultats 132 repeats, we have unmasked the presence of an unmethylated MIR repetitive element besides the Complexin 2 (CPLX2) CpG island, both of which undergo extensive DNA methylation in tumors and cell lines (Figure 1C). Further characterization of the genomic region around the CPLX2 gene, has unveiled a novel set of genes undergoing promoter DNA methylation (Figures 2B and 3C). Even though the region affected does not encompass a complete chromosomal band, as reported by Frigola and collaborators for 2q14.2, the number of genes affected by the presence of promoter DNA methylation is significant and further experiments directed to ascertain the possible roles of these genes in carcinogenesis are warranted. These findings, together with unpublished results from our laboratory and previous reports29,30 suggest that promoter DNA methylation affecting multiple neighboring genes is not an uncommon event in colorectal carcinogenesis and surely deserves further investigation.
Results clearly indicate that hypermethylation of HRH2, CPLX2, AK124.837 and SNCB is a highly frequent event in colorectal carcinogenesis (Figure 3C), affecting well over 60% of the tumors tested. Moreover, hypermethylation was also detected in a significant fraction of the adenomas, thus suggesting an early onset of the methylation in this region during tumorigenesis. These results are extremely relevant, as early detection of tumors is one of the most critical parameters affecting patient survival (reviewed in35). Our results show that only a very small fraction of all the tumors analyzed do not contain any methylated gene (less than 2% of the samples). Future experiments will elucidate the usefulness of this region as a diagnostic and/or prognostic marker, and will assess the feasibility of its detection in different samples (i.e., the analysis of stools or circulating DNA).
Additionally, even though the highest methylation levels have been found restricted to tumors, low to moderate DNA methylation levels have also been detected in normal tissue from different patients (Figure 2B). Normally methylated genes include the alternative transcript AK124.837 (CpG125), BC042064 (CpG78) and GPRIN1 (CpG114). Low level DNA methylation was found over CpG79, associated to SNCB, in only one normal sample. Interestingly, BC042064 gene is located within a segmental duplication that has evolved from a sequence located further downstream chromosome 5 in 5q36.1. Methylation of this region in the normal tissue suggests that it may have some specific feature that renders it prone to become methylated, hypothesis that surely deserves further investigations.
DNA-methylation and histone signatures define active and inactive chromatin domains in 5q35.2 The genomic region presented here represents a unique model into which further investigate the complex regulatory mechanisms that lead to transcriptional inactivation associated to DNA methylation. As we have mentioned above, the presence of highly expressing genes among the DNA-methylated, silenced genes, provides a collection of positive and negative controls that can be directly compared, therefore allowing the identification of common features that define the different transcriptional states. In the above scenario, we provide evidence for the presence of isolated expression domains characterized by specific patterns of histone modifications and DNA methylation. The active compartments, where SFXN1, THOC3, RNF44 and TSPAN17 are embedded, are characterized by many active-associated chromatin features: high transcription
Resultats 133 rates are associated with unmethylated promoter CpG islands (Figures 5A and 5C), and moderate to high levels in the promoter regions of the active histone modifications Ac H3K9, Ac H4K16 and H3K4me3, and low or inexistent levels of the inactive histone modification H3K27me3 (Figure 5B). On the other hand, the inactive compartment is defined by low transcriptional activity associated with methylated promoter CpG islands (Figure 5A and 5B), low levels of the active histone modifications Ac H3K9, Ac H4K16 and H3K4me3, and moderate to high levels of the inactive H3K27me3 histone modification mainly over the DNA methylated CpG island (Figure 5B).
The fact that transcriptionally active and inactive regions are arrayed one after another (Figure 5A) lead us to think that some insulator mechanism must operate in the region. Only one factor with insulator activity has been identified in mammals so far, the CTCF factor, which is essential in the formation of differentially methylated imprinted domains (Reviewed in36). The use of global approaches to map CTCF targets genome-wide in the human genome have identified many non-imprinting related locations at which CTCF may regulate distinct epigenetic states, some of them across the region described here37. Some of these CTCF sites in 5q35.2 are found at intergenic regions between genes with opposite transcriptional and chromatin states (Supplementary Table 5), in agreement with a CTCF role in marking boundaries of different histone methylation domains37. Future experiments should uncover a possible role for this insulator protein in the maintenance of the transcriptional silencing in this region.
As shown in figure 4B, there is a global trend towards downregulation affecting most of the genes in the region, with the exception of THOC3 and RNF44, which appear upregulated in the tumors. The study of a set of differentially expressed genes indicates that only low expressing or silent genes in the normal tissue become DNA methylated in both tumors and cell lines, in agreement with previous reports on DNA methylated genes in cancer28. These results, together with the fact that transcriptional downregulation affects many unmethylated genes in the region, are consistent with the hypothesis that DNA methylation is not the primary event leading to transcriptional silencing in carcinogenesis, but rather locks in an already existing silent state. Nevertheless, DNA methylation plays a dominant role in the silencing mechanism, as the silent genes cannot be reactivated unless first demethylated. Thus, the use of drugs that inhibit HDAC activity such the one used here has no effects on the transcriptional activity of DNA-methylated genes but rather synergize with 5azaC once the DNA has been demethylated (Figure 6 and Supplementary Figures 2 and 3). Moreover, the inhibitory effects of DNA methylation extend over non-CpG island containing genes such as PcLKC and unmethylated genes such as SNCB, which can be reactivated after 5azaC treatment alone, as has been previously shown for other genes29.
The fact that some methylated genes appear upregulated in a limited number of tumors could be explained due to contamination of the tumor sample with normal cells. Alternatively, we could also hypothesize that only a small fraction of the cells in the tumor present a hypermethylated promoter, thus masking the effects of methylation on the expression levels. Both situations could be responsible for the results obtained.
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A stem cell-like chromatin landscape defines silencing across 5q35.2 To date, the origins of human cancer remain obscure. A very exciting and promising possibility comes from recent studies of the epigenetic landscape in murine embryonic stem (ES) cells, where a striking balance of the two opposite histone modifications, the “active mark” H3K4me3 and the “inactive mark” H3K27me3, exist over the promoter regions of key developmentally- regulated genes, in what the authors call bivalent domains24. The presence of these two marks over the subset of genes reported by Bernstein and colleagues correlates with low levels of transcription of the same genes, which suggests that bivalent domains keep key developmentally-regulated genes in a silent state, but ready for activation, which reflects the pluripotency of these cells at the chromatin level24.
A possible origin of cancer from stem or early progenitor cells has been previously postulated38- 41. Supporting evidence to this hypothesis has come from the discovery that promoter regions of genes frequently hypermethylated in colorectal cancer tend to be occupied by polycomb proteins in human embryonic stem cells, even though the authors do not provide evidence for polycomb protein occupancy in cancer cells22. Furthermore, the discovery that bivalent domains embody the promoter regions of a subset of silenced genes in embryonic carcinoma (EC) cells, the malignant counterparts of ES cells23, indeed suggests that cancer cells have a distinctive stem cell-like chromatin pattern. In this context, DNA methylation has been suggested to be the factor locking in these chromatin patterns in adult cancers, which in contrast to ES and EC cells, display high levels of promoter DNA methylation23.
Consistent with the above mentioned, in the HCT116 cell line both H3K4me3 and H3K27me3 have been found over the promoters of all DNA-methylated genes and the DNA unmethylated, but severely downregulated SNCB gene, across 5q35.2 (Figure 5B). Noteworthy, SNCB is not methylated in the HCT116 cell line, but it is heavily methylated in multiple cell lines and tumor tissues (Figures 2B, 3C and 4A). The bivalent domains reported here embody areas of low expression, discontinued by active domains enriched for the active mark H3K4me3 and depleted for the H3K27me3 inactive mark. Unlike embryonic EC, where bivalent domains keep key genes ready for activation with DNA-methylation free promoters23, HCT116 cancer cells have dense promoter DNA methylation, which prevents any further transcription activation. Concurrent methylation of multiple neighboring CpG islands has been previously reported in colorectal cancer29,30, and genome-wide maps of DNA methylation in the colorectal cancer cell line CaCo2 showed that DNA methylated genes tend to cluster together28. Our results not only provide another example of concurrent DNA methylation of multiple neighboring CpG islands in cancer, but also indicate that concurrent DNA methylation occurs at genomic regions where genes under the control of bivalent domains cluster together.
Ooi and colleagues have recently reported on a previously unnoticed association between DNA methylation and histone H3 tail42. Their results indicate that unmethylated Histone H3 at lysine 4 can lead to DNA methylation mediated by the DNA methyltransferase DNMT3L, therefore linking the absence of a key active histone modification to DNA methylation. Nevertheless, the DNA-methylated genes reported here contain moderate levels of methylated H3K4 and
Resultats 135 therefore the results obtained by Ooi et al. do not provide a convincing explanation as to why these genes become DNA-methylated.
According to the presence over the promoters of silenced gens of H3K27me3, further chromatin immunoprecipitation experiments have revealed the presence of the PRC2 member histone methyltransferase EZH2, responsible for the deposition of this histone modification (Reviewed in26) and the member of the PRC1 BmiI. Interestingly, together with EZH2 and BmiI we have also detected the presence of the histone deacetylase SirT1, which is in agreement with the specific enrichment of acetylated H4K16, the lysine residue deacetylated by SirT1, seen over the promoters of silenced genes upon drug treatment (Figure 7). This class III HDAC has been shown to be directly involved in the repression of DNA-methylated genes in different cancer models34 and is associated to transcriptional repression mediated by polycomb in Drosophila43. Interestingly, experiments conducted by Kuzmichev and colleagues44 have allowed the identification of a previously undescribed polycomb complex, named PRC4, which in addition to the basal components EZH2 and SUZ12 contains the SirT1 HDAC and the Eed isoform Eed2. As noted by Kuzmichev et al., the fact that PRC4 shows preference for methylating H1K26, a target for deacetylation by SirT1 in vivo, implies that PRC4 can also methylate H3K27 or alternatively, that another polycomb repressor complex methylates this position44. The fact that Eed2 is a specific isoform expressed exclusively in cancer cells and undifferentiated embryonic stem (ES) cells, further strengths the hypothesis of a cancer-stem cell origin of cancer. Based upon this observations, we conclude that the PRC4 complex is associated with the deposition of H3K27 mark and the specific depletion in the acetylated form of lysine 16 in histone H4 over the silenced genes, which together with a specific combination of other histone marks defines transcriptional silencing across 5q35.2
Bivalent domains persist after drug-induced gene reactivation Comparison of DNA methylation between the normal tissue and the tumor tissue and cell lines has revealed the existence of tumor-specific DNA methylation (Figure 2B and 4C), which chromatin immunoprecipitation experiments over the HCT116 cell line have linked to the presence of bivalent domains and histone hypoacetylation (Figure 5B and 5C). Nevertheless, we can not confirm that the trimethylated mark on lysine 27 is cancer specific, mainly due to the absence of a cellular model that resembles the normal colorectal epithelium in which to perform such experiments. In this context, recent works focused on the genomic distribution of different histone methylation signatures in murine ESCs have revealed the presence of bivalent domains exclusively over the same genes described here45, with some major differences in the case of human ESCs46,47 (Supplementary Table 5). Similar experiments performed over human embryonic stem cells48 failed to detect the polycomb proteins SUZ12 and Eed and the methylated H3K27 over the same promoter regions, probably indicating different onset times for the chromatin patterns over these genes in different model organisms and different cell types. Together, these results indicate that H3K27me3, together with low levels of H3K4me3, are already present at the genes that will become DNA-methylated during carcinogenesis.
As shown before for a subset of genes, bivalent domains in murine ESCs tend to become selectively enriched for any of the two opposite marks upon cell differentiation. Therefore, it
Resultats 136 could be predicted that the bivalent domains described here would also become enriched for the active mark H3K4me3 and depleted for the inactive mark H3K27me3 upon drug-induced reactivation. Unexpectedly, both H3K4me3 and H3K27me3 were found enriched upon reactivation, therefore suggesting that cellular memory systems prevent these bivalent domains from being perturbed. In agreement with these results we have found that EZH2, the enzyme responsible for H3K27me3 deposition, tends to be enriched in most of the promoter regions analyzed after drug treatment in comparison to the untreated cell line (Figure 7), thus providing an explanation for the H3K27me3 increase. These results are consistent with previous reports on drug reactivation of DNA methylated genes, which also showed that both H3K4me3 and H3K27me3 become enriched after gene reactivation, although the authors show that EZH2 levels decrease at the same promoters49.
Spreading of H3K27me3 to adjacent active domains upon drug treatment We have shown how the EZH2 protein increases at promoters of most genes across 5q35.2, regardless of their basal level in the untreated cells (Figure 8), in agreement with the increase in the H3K27me3 levels at silenced promoters (Figure 7). But unexpectedly, H3K27me3 mark lost its specific dispersed distribution after drug co-treatment, showing a dramatic increase over the promoter regions of active genes (Figure 7). This enrichment provides a full explanation for the downregulation that this subset of active genes suffer after 5azaC and TSA co-treatments, together with a depletion of the H3K4me3 mark over the same promoters. Therefore, by using 5azaC and TSA we have broken the balance of H3K4me3 and H3K27me3 exclusively over the promoters of active genes, while silenced genes retain their bivalent domains, which further provides evidence for the existence of isolated chromatin and expression domains.
We provide further evidence for a dominant role of H3K4me3 and H3K27me3 over the rest of histone modifications tested here. In this context, even though we have seen a global increase in the lysine 9 acetylation levels at all sites tested across 5q35.2, breaking the balance between the polycomb and trithorax marks at the active promoters has been sufficient to provoke their transcriptional downregulation.
Summarizing, our data clearly support the hypothesis that genes marked with bivalent domains become preferentially DNA methylated during carcinogenesis, although at present we can‟t give a reasonable explanation for this phenomenon. The bivalent domains over the promoters of DNA methylated genes described here exist in undifferentiated ESC from both human and mouse, with some major differences, therefore leading to the conclusion that DNA methylation locks in silence at genes that are already kept in a low transcriptional state by the presence of these domains. Drug reactivation not only fails to erase bivalent domains, but also induces the spreading of the silencing mark, most probably by breaking the insulator mechanisms that keep neighboring genes in distinct and opposite transcriptional states.
Resultats 137
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Figure Legends
Figure 1. Panel A contains a fragment of a polyacrilamyde denaturing gel displaying the Ap1 band (arrowhead). The presence of the band in all normal tissues indicates the unmethylated state of the SmaI sites at both band ends in the normal colon. Loss of band intensity (marked by an asterisk) in tumor samples 53, 81 and 99 is indicative of a tumor-associated methylation of one or both of the SmaI target sites. Therefore these three normal-tumor pairs are used as Ap1 positive methylation controls for subsequent analysis. Tumor samples 72 and 74 do not display any band intensity loss, and are considered as negative methylation controls. Genetic structure of the region containing the amplified band is depicted in B. DNA is represented by solid lines and the amplified Ap1 fragment, the CPLX2 CpG island, and the MIR and MIRb repetitive elements are represented by black boxes. CpG dinucleotides are represented as short vertical lines across a horizontal line (DNA). Taking into account CpG distribution and Ap1 band location, two bisulfite sequencing fragments were designed (gray boxes), one of them located over the CPLX2 CpG island and a second fragment located over the MIR repetitive element (named CPLX2 MIR), covering each fragment one of the two SmaI sites. Methylation data from direct sequencing of bisulfite-treated DNAs (C) was obtained for the 5 normal-tumor pairs in A and for a collection of 8 colorectal cancer cell lines. Each CpG dinucleotide is represented by circles. Sequencing data clearly corroborates the unmethylated state of all normal tissues and the tumor-specific methylation of both CPLX2 CpG island and MIR element in methylation positive controls and cell lines, with the exception of CaCo2 and SW480 cell lines. In the case of SW480 cell line, the unmethylated status of the CPLX2 CpG island correlated with detectable transcription levels of CPLX2 (D).
Figure 2. The genomic region containing the CPLX2 locus is located near the 5q telomere at 5q35.2 (A). The region covered in this study spans over 1.2 Mb and includes 18 genes, from most centromere-proximal DRD1 to most telomere-proximal TSPAN17. CpG islands (CpGi) across 5q35.2 are named according to the number of CpG residues they contain, except for the CPLX2 MIR fragment, which is not a CpG island itself. Direct sequencing methylation data for seven of the eight colorectal cancer cell lines and the 5 normal-tumor pairs is summarized in B. Each box represents a CpG island, which can be found unmethylated (white), partially methylated (gray) or heavily methylated (black). ND, not determined.
Figure 3. Real Time melting curve analysis was used to determine methylation status of 4 of the de novo methylated genes in a series of 121 normal-tumor pairs that included both adenomas and carcinomas. Panel A contains melting curve analysis for a negative methylation control (Case 861) and a positive methylation control (case 659). Only positive control tumor 861 displays an increase in the melting temperature of the amplified DNA fragment with respect to the melting temperature of the normal tissue. No melting temperature increase is seen for negative control case 861. Even though methylation affected both adenomas and carcinomas, the latest exhibited, on average, a higher number of methylated genes (B). Panel C contains methylation data summary for the 4 analyzed genes (Y axis) in 118 of the 121 normal-tumor pairs (X axis; 3 carcinomas failed to amplify 1 or more genes and therefore are not shown). Each white box corresponds to an unmethylated status and a black box to a methylated status.
Resultats 141
Carcinomas are clustered together (samples to the left) and represented in order of their methylation frequency. Adenomas (Ad) are clustered at the right side of the panel and are also represented in order of their methylation frequency. The percentage of tumors with each methylated gene is given.
Figure 4. Expression ratios and promoter DNA methylation status were determined for 12 genes across 5q35.2 in a series of 16 normal-tumor pairs and HCT116 cell line (A). Expression values are represented as the log2 of the tumor-normal ratio. Therefore, genes that are upregulated in the tumor have values above 0 and genes downregulated in the tumor have values below 0. Those expression bars colored in red correspond to a methylated promoter and those colored in green correspond to unmethylated promoters. PcLKC is the only gene in this study that doesn‟t have a promoter CpG island, and therefore its methylation status is referred here as unmethylated (green). Mean values of the expression ratios of all 16 normal-tumor pairs are plotted in B. Expression ratios and methylation data for the HCT116 cell line are given in C. In this case, values for each gene were obtained as the ratio of the HCT116 cell line expression and the mean expression values of the 16 normal tissues. Panel D represents mean absolute expression values across the same 12 genes across 5q35.2 for the 16 normal tissues (green), the 16 tumor tissues (red) and HCT116 cell line (black). The absolute expression values of the HCT116 cell line were compared to the absolute expression levels of a panel of other colorectal cancer cell lines used in this study (E). ND, not determined.
Figure 5. Absolute expression data for genes across 5q35.2 (A) were compared to the levels of different histone modifications (B) in the cell line HCT116. IgG values represent the negative control. The presence of the three activation marks Ac H3K9, Ac H4K16 and H3K4me3 and the inactive mark H3K27me3 was ascertained. Regions analyzed included promoter regions of all genes, and for three additional genes (HRH2, CPLX2 and SNCB) the CpG island (C) and a distal intronic region (I) were analyzed besides the promoter region (P). In those cases where the amplified region falls inside or nearby a CpG island, the CpG island methylation status is noted as methylated (gray boxes) or unmethylated (white boxes). Chromatin immunoprecipitation values are shown as the enrichment fraction over input. ND, not determined.
Figure 6. Absolute expression data for genes across 5q35.2 in the cell line HCT116 after drug treatment. White bars correspond to the untreated control cell line, while light gray corresponds to the 5azaC treatment and dark gray corresponds to the double 5azaC and TSA treatments.
Figure 7. Chromatin remodeling across 5q35.2 after drug treatment in the HCT116 cell line. The same histone modifications previously characterized are shown in relative levels to the untreated control cell line. Light gray bars correspond to the 5azaC treatment and black bars correspond to the double 5azaC and TSA co-treatment. Values above 0 correspond to a modification enrichment while values below 0 correspond to modification depletions in the treated cells. Dashed boxes group values for different primer sets in the same gene, corresponding to the promoter (P), CpG island (C) and intron (I) regions. ND, not determined.
Resultats 142
Figure 8. Mapping of polycomb components EZH2 and BmiI and the SirT1 HDAC across gene promoters in 5q35.2. Absolute enrichment levels over the input are shown for the untreated control HCT116 cell line (white bars), 5azaC treated (light gray) and 5azaC and TSA co-treated cells (dark gray). Chromatin immunoprecipitation values are shown as the enrichment fraction over input.
Resultats 143
A 53 81 99 72 74 N T N T N T N T N T
Ap1 * * *
B 100 bp 5‟ 3‟ Amplified fragment CPLX2 CpG island MIR MIRb
CpG distribution
Bisulfite sequencing fragments
C 53 N T
81 N T
99 N T
72 N T
74 N T
LS174T HT29 KM12C HCT116 LoVo DLD-1 CaCo2
SW480
Methylation status 0% 25% 50% 100% Not determined
D
1
-
HCT116
SW480
CaCo2
KM12C LoVo DLD
200 pb CPLX2
500 pb 18S rRNA
FIGURE 1
Resultats 144
A Chromosome 5
0 250 500 750 1000 1250 Kb 5q35.2
Genes
CpGi B
HCT116 HT29 N KM12C D N LoVo D DLD1
N CaCo2 D N SW480 D
N N 53 D T N 81 T N 99 T N 72 T N N D 74 T N D
FIGURE 2
Resultats 145 C A AK124.837
Fluorescence CPLX2 0.062 0.152 0.242 0.332 HRH2 SNCB 65 Tumors Case 861 70 75 Temperature N N T / 80 85 90
Fluorescence 0.199 0.319 0.079 65 Case 659 70 75 Temperature 80 N T 85 90 B
% samples 10 15 20 25 30 35 40 0 5 0 1 Methylated 2 Ad genes 3 tumors % methylated of 70,8 72,7 68,6 63 Carcinoma Adenoma 4
FIGURE 3
Resultats 146
A 10841 10842 10851 3 3 3 2 2 2
1 1
) ) ) 1 0 0 0 -1 -1 -1
-2 -2 -2
Log2(T/N Log2(T/N -3 Log2(T/N -3 -3 -4 -4 -4 -5 -5 -5 -6 -6 -6
10854 10861 10876 3 3 3
2 2 2 ) 1 1 ) 1 0 ND 0 0 -1 -1 -1
-2 -2 -2 Log2(T/N
-3 -3 Log2(T/N -3 Log2(T/N) -4 -4 -4 -5 -5 -5 -6 -6 -6
10886 10845 10847 3 3 2 3 2 1 ND 1 1 0 0 -1 -1 -1 -2 -3 -2
-3
Log2(T/N) Log2(T/N) Log2(T/N) -3 -4 -5 -4 -5 -7 -5 -6 -6 -9
10872 10665 10846 3 3 3 2 2 1 1 1 ND 0 0 -1 -1 -1 -3 -2 -2
-3 -3
Log2(T/N) Log2(T/N)
-5 -4 -4 Log2(T/N) -7 -5 -5 -6 -6 -9
10659 10661 10663 3 3 3 2 2 2 1 1 1 0 0 0 -1 -1 -1 -2 -2 -2
-3 Log2(T/N) -3 Log2(T/N) -3 -4 Log2(T/N) -4 -4 -5 -5 -6 -5 -6 -7 -6
10879 Mean ratios all cases HCT116 ratios 2 B 3 C 2 3 1 1 ND ND 1 0 0 -1 -1 -1 -2 -3
Log2(T/N) -5 Log2(T/N) -2 -3 -4 -7 -3 -5 -9 -4 -6 Log2(HCT116/meanN) -11
Mean Normal HCT116 D E KM12C Mean Tumor LoVo 1.00E+00 1.00E+00 DLD1 HCT116 CaCo2 SW480 1.00E-01 1.00E-01 1.00E-02 1.00E-02 1.00E-03 1.00E-03 1.00E-04 1.00E-04 1.00E-05
1.00E-06 1.00E-05 Expression relative to controls to relative Expression Expression relative to controls to relative Expression 1.00E-07 1.00E-06
FIGURE 4
Resultats 147
1.00E+00 A 1.00E-01 1.00E-02 Gene 1.00E-03 Expression 1.00E-04 1.00E-05 1.00E-06 1.00E-07
B 0.50 0.40
0.30 IgG 0.20
0.10
0.00
2.00
1.50
Ac H3K9 1.00
0.50
0.00
0.50
0.40
0.30 Ac H4K16 0.20
0.10
0.00
30
25
20 H3K4me3 15
10
5 ND 0
1.00
0.75
H3K27me3 0.50
0.25
ND 0.00 C CpGi status P C I P C I P C I
FIGURE 5
Resultats 148
DRD1 SFXN1 HRH2 6,00E-03 2,50E-01 2,00E-04
5,00E-03 2,00E-01 1,50E-04 4,00E-03 1,50E-01 3,00E-03 1,00E-04 1,00E-01 2,00E-03 5,00E-05 1,00E-03 5,00E-02 0,00E+00 0,00E+00 0,00E+00
CPLX2 AK124.837 THOC3 1,00E-03 6,00E-05 1,00E-01
8,00E-04 5,00E-05 8,00E-02 4,00E-05 6,00E-04 6,00E-02 3,00E-05 4,00E-04 4,00E-02 2,00E-05 2,00E-04 1,00E-05 2,00E-02 0,00E+00 0,00E+00 0,00E+00
1,20E-02 CLTB 8,00E-03 RNF44 4,00E-04 PcLKC
1,00E-02 6,00E-03 3,00E-04 8,00E-03 6,00E-03 4,00E-03 2,00E-04
4,00E-03 2,00E-03 1,00E-04 2,00E-03 0,00E+00 0,00E+00 0,00E+00
2,00E-02 SNCB 8,00E-02 TSPAN17
1,50E-02 6,00E-02
1,00E-02 4,00E-02
5,00E-03 2,00E-02
0,00E+00 0,00E+00
FIGURE 6
Resultats 149
3 2,5 2 1,5 1 0,5 Ac H3K9 0 -0,5 -1 -1,5 -2
1,5 1 0,5 0 Ac H4K16 -0,5 -1 -1,5 -2
4
3
2
1 H3K4me3 ND 0
Log2 (treated/untreated HCT116) (treated/untreated Log2 -1
-2 5
4
3
2 H3K27me3 1 ND 0
-1 P C I P C I P C I
FIGURE 7
Resultats 150
HCT116 - 5azaC 5azaC + TSA 1.4
1.2
1.0
0.8 BmiI 0.6
0.4
0.2
0.0
1.6
1.4
1.2
1.0 EZH2 0.8
0.6
0.4
0.2
0.0
1.8
1.6
1.4
1.2 SirT1 1.0 0.8
0.6
0.4
0.2
0.0
FIGURE 8
Resultats 151
TSPAN17 SNCB GPRIN1 PcLKC3' PcLKC5' RNF44 CLTB BC034407 BC042064 THOC3 AK124.837 CPLX2 HRH2 SFXN1 DRD1 Gene
1. Table Supplementary
fromcount end5‟ d. Total distance between most distal ends CpG145 (DRD1) and CpG47 (TSPAN17): 1,203,869 bp. Ratio c. theof observed (O) and expected (E) CpG d b. Ratio of the obser a. Base positions
CpG island CpG 114 110 126 100 125 118 145 47 79 91 47 78 52 72 93
based on March 2006 build the of
ved CpG dinucleotides and the CpG island length
176006745 175989127 175969069 175956384 175902356 175896105 175775725 175558573 175420174 175327533 175231122 175156216 175017611 174837949 174803360 CpG island CpG position
Characteristics and positions of CpG islands across 5q35.2 across islands of CpG positions and Characteristics ------
176007229 175990100 175970163 175957257 175903659 175897722 175776450 175559091 175421036 175327989 175232672 175157285 175018362 174838671 174804951 a
Lengthbp
UCSC genomeUCSC browser
inucleotides in each CpG island 1099 1304 1618 1551 1070 1592 485 974 8 726 519 863 457 752 723 74
%G+C 71.1 55.4 63.1 52.8 61.6 76.9 70.7 68.7 70.5 67.2 71.8 57.8 74.3 65.2 62
%CpG 19.4 16.2 20.7 20.8 16.9 15.6 27.5 18.1 18.1 22.8 16.1 22.1 19. 25.7 18.2
1
b
Ratio O/E 1.09 1.04 1.08 1.21 0.82 0.93 0.72 0.77 0.93 0.72 0.86 1.16 0.94 0.86 0.8
c
Relativeposition
Base positions
to CpG118to - - - 849460 831842 811784 799099 745071 738820 618440 401288 262889 170248 137854 317545 351265 73837 0
d
Resultats 152
CpG47 CpG79 CpG114 CpG91 CpG110 CpG126 CpG100 CpG47 C CpG52 CpG125 CpG118 CpG72 CpG93 CpG145 island CpG
Supplementary Table Table Supplementary
arePrimers shown a. pG78
Base positions
TSPAN17 SNCB GPRIN1 GPRIN1 None RNF44 CLTB BC034407 BC042064 THOC3 AK124.837 MIR CPLX2 CPLX2 HRH2 SFXN1 DRD1 Gene
based on March 2006 build the of 175989268 175989476 175969000 175903332 175420128 position Product 176006967 175956397 175896049 175775945 175558616 175327833 175231657 175157473 175157004 175018104 174837971 174804213
in tothe 5‟ 3‟ orientation
------
175969337 175969337 175903548 175420425 176007253 176007253 175956634 175896291 175776359 175558855 175328014 175231864 175157757 175157373 175018391 174838285 174804583
2
. PCR primers used for bisulf for used primers PCR .
a
GGATTGAGTTGATTTATGGG TTTTTGGTTATGGATAGGTTT GTATTTTGGGTGATAGGTGGT GTTTGTGATTAAGAAGGGAGT TAATAGTTG AGAAGAAATTAGGTGGTTATAT GAGAAGAAGTTAAAGTTATTAGT AGTTGTTTTGTTTGTTTTAGGAT GATGTATTTGATTTTGAGGATATT GAGGGTTTTATGGTTGTAG GGAGATTTAAGTTTGAATTTAG AGGTTTTTAAATAGGGAAGT GGTTTAGTTAAGAGTGGGT GGGTGGATTTGGAAAGTGT AAATGGAATATAGTGGTTTTAGT TTTTAATTAGGGTTGGAGATGT outer primer Forward
TTTGGGGTTAGT
UCSC genomeUCSC browser
ite genomic sequencing. genomic ite ATTCCTAAAAATACTAATACTTAC CCCCAAATTTCAAAAACACTAA AAA AATACTAAACATAACCATCCAA ACAAAAACTACCACCACCTA TCCCCTCTCCTACAACTAAA CAAAATAAATCAAAAACCAAATCTA CCAAAACCACTATCTCTCCAA TTACAATTACAACTCAAACACTA AACCAAATATTACTAAATAAACAA CCCCCATCCCCTATCAATA TTACCCAAAATCATATACTAA TACAACCTCCCAAATCTAAA TTACTCTACTCAT TTCCCCACCCACCTAACTA TTTCAAATTCTAACAATCATAAAC outer primer Reverse TATCTATTTCCAAACTCCTC
CCCACAA
GATTTTAAAGGGATTGAGGGT GGTTTGGGGAGGGTGATAT TGTGTATGAGTTTAATGGAATGAT GTATAAAATGGGGGTAGATTAT ATAGTTGTTTGGGGTTAGTAG GTGGGGTTTTGGGAAGTTTAT None GTTAGGTTTTGGGTATTAGTT None TTTTAATTTATAATATTGTAGTAGT TTAAGTTTGAATTTAGAGAATAAT TTTAAATAGGGAAGTTAG GGTAGAGGGGAAATAGGT None TGAGGATGGGTTTGGAGTTT TTCCCTATTTTCAAATTCTAACAA inner primer Forward
TTT
None None AACCAAACTAACCTAACTATAAA AACCATCCAAAAACATCTAAAA CCCTAATCCAAATTAACTCTAA CACTTCCTTTATTATTACTTTTA ACAATACCTAACCCAAATATCC TACTCACAAAAATAAATCTTACTA ATACACAACCTACCTAATCTAAA None TCTCCTCCCCTCCACCTA CACTCTACTATTCTCCTTA CCCCCCTACCTTCTCTA TCCAAATATCCCCAACAAAA CCCACCCACCTAACTAAAC None inner primer Reverse
T
length (bp) length Product Product 287 2 338 23 217 24 415 24 298 182 208 285 370 288 315 371 09 8 3 0
Resultats 153
Supplementary Table 3. PCR primers for expression analysis.
Gene Product positiona Forward primer Reverse primer Product length
DRD1 174801483 - 174801614 CAATGGGGCCGCGATGTTTT ATGCCAGCTGCCTCCTCCTTTTT 132
SFXN1 174869765 - 174871765 ATGCCGTCGTCAATTACACCAACAG CTACGGCAGCAAAGGGAACAAAAC 174
HRH2 175017711 - 175042475 CGCGGACCGAGGCGAACC GAATCCGAGGCACTGTCTGG 211
CPLX2 175238651 - 175239628 GAGGCGGAGCGGGAGAAGGTC GCCCGGGCAGGTATTTGAGCA 214
AK124837 175231117 - 175239969 GGGAGCGGTAGAACGTCAGGGTAT AGTGGGGAGCAGGGAGGAATGTAT 159
THOC3 175326847 - 175327758 GATGGCCCCGTGGTGCTCAG CCCATGTCCCCGATAATTGTTTTCT 238
CLTB 175752420 - 175757578 GCAAGTGGCGAGAGGAGCAGAGGA CGAGCGCAGGCGGGACACAT 287
RNF44 175888572 - 175889154 ACAGCCATCAGTCGGAGCAGA GGCCCCACCCACAAGTTTC 251
PcLKC 175950826 - 175952388 GAAGCTTCAAGCTATGAAGG CCTTGATTTCCTGACTGTTC 245
SNCB 175980367 - 175986061 AACATCGCAGCAGCCACAGGAC GGGCAGGGACAGGGACAGAAT 231
TSPAN17 176011408 - 176012474 CCCCGTGTGGCTGTTTGTGGTAGT CCGGTAGGCCTTGACGTTGTTGTT 226
a. Base positions based on March 2006 build of the UCSC genome browser Primers are shown in the 5‟ - 3‟ orientation
Resultats 154
Supplementary Table 4 Table Supplementary TSPAN17 SNCB PcLKC RNF44 CLTB THOC3 CPLX2 HRH2 SFXN1 DRD1 Gene
arePrimers shown in the 5‟ b. Base positions a. CpGi, CpG island
Promoter+CpGi Fourth exon CpGi Promoter+Cp Promoter Promoter+CpGi CLTB promoter THOC3 promoter CPLX2 distal promoter Second intron CpGi Promoter First intron CpGi Promoter Promoter+CpGi Promoter+CpGi Region
a
Base positions
- intron Gi
–
.
3‟ orientation3‟
Primer sets Primer
176006849 175985907 175989488 175990064 175908639 175897523 175776609 175328169 175230775 175186220 175157105 175155821 175037215 175018145 175017179 174837851 174803937 Productposition
based o
n Marchn 2006 build of the - - - - - – ------
176007062 175986046 175989660 175990302 175908817 175776822 175328287 175230954 175186399 175157277 175156051 175037372 175018361 175017347 174837971 174804181
175897694
for Real Time PCR ChIP analysis. PCR Time for Real b
TGCGAGGAGGTCCGAATCAG GGGGCGGGGATGGTGACA GCCACTGCCTCGGTTATCC CAGCCACCGCTCGTTCCT ACGGGGGCTCTGGCATCA GCCGCCCCACTTCTCCTTGTT AGGCCGTGTTGTTTTGTGTATTTT TTCTTCTGGGGTTGTTCCGTAATC ACCCCTTGCCCCCACCACT TGGATGTGGGAAGGAGACGAAGAA CCTCCAGACCCCCACCCCATCC TGAAGGATCTCGTCAAAAAGTTAG ATAACCCCTCCCAGCCCATTTTG TTTTGTCTCCGGGTCTGGGTGGTG TATTTCCCCCTTCTAGCCTGACTC GTGAACGGATGCTGGCTACG GCCGGCCGTTCTAGGAGTTGGT Forwardprimer
UCSC genomeUCSC browser
GACCGCCCTCAGCATCCAG ACAGGACTGGTGAAGAGGGAGGAAT AGCGTCTGCGTGTCCTGTG CCTCAACTCCCAACAGACTAC GGACAGGAGGAAGGGACC GGGCGTGGCGGTTAAATGTCTC CAGGCAGGGAGGAGGCATTCAGA CACACCCGCTAGCCCTTTTCAT CGTCTCCTTCTCCCTCCATTCC CA AGGCTTCCCCGCGGCTTCTCAG GCAGTTCTGCCCTGAAGTTC GCACCGCCCAGT GCTGAGCGCTGCCTGTTGTGG AGATTGTGAGCCCGCCCTTGA ACGGTGGTGTGGG GAGGACGCCCCGGTGAGTGC Reverseprimer
GTTTTGAGGGAGTGGGTGTTGG TCCTCATTCT
GCTCTG
Productlength 214 140 173 239 179 172 214 119 180 180 173 165 158 217 169 121 245
Resultats 155
Supplementary Table 5. Bivalent domains and CTCF binding across 5q35.2 in different cell types
Gene HCT116 cellsa Murine ESCb Human ESCc Differentiated CTCF sitesd Human cellsd
DRD1 Bivalent Bivalent Bivalent H3K27me3 SFXN1 H3K4me3 H3K4me3 H3K4me3 H3K4me3 SFXN1 and HRH2 HRH2 Bivalent Bivalent None H3K4me3 HRH2 and CPLX2 CPLX2 Bivalent Bivalent H3K4me3 H3K27me3 AK124.837 Bivalent Bivalent H3K4me3 H3K27me3 THOC3 H3K4me3 H3K4me3 H3K4me3 H3K4me3 THOC3 and BC042064 CLTB H3K4me3 H3K4me3 Bivalent H3K4me3 CLTB CpG island RNF44 H3K4me3 H3K4me3 H3K4me3 H3K4me3 PcLKC Bivalent H3K4me3 None None Intragenic PcLKC SNCB Bivalent Bivalent Bivalent H3K27me3 TSPAN17 H3K4me3 H3K4me3 H3K4me3 H3K4me3 Downstream TSPAN17
a. Data summary from Figure 5B b. Data summarized from [Mikkelsen, 2007 #270] c. Data summarized from [Pan, 2007 #296] and [Zhao, 2007 #297] d. Data summarized from [Barski, 2007 #272]. Where two genes are shown, CTCF site maps at intergenic space between them Primers are shown in the 5‟ – 3‟ orientation
Resultats 156
Supplementary Figures
0.50
0.40
0.30
IgG 0.20
0.10
0.00
0.50
0.40
0.30 H3K9me2 0.20
0.10
0.00
0.50
0.40
0.30 H3K9me3 0.20
0.10
0.00
0.50
0.40
0.30 Ac H4K12 0.20
0.10
0.00
3.00
2.50
2.00 Ac H3K14 1.50 1.00
0.50
0.00 P C I P C I P C I
Supplementary Figure 1.
Levels of different chromatin modifications compared to the IgG negative control. Both H3K9me2 and H3K9me3 marks, associated to inactive chromatin, are found at very low levels across the region. Acetylation of the lysine 12 in histone H4 is also detected at low levels while acetylation of the lysine 14 in histone H3 is found at high levels across the whole region. All values are expressed as absolute levels over the input fraction.
Resultats 157
#72 HRH2 CpG island
HCT116 Control AZA 48h ND TSA 24h AZA + TSA ND
#118 CPLX2 CpG island
HCT116 Control AZA 48h TSA 24h AZA + TSA
#125 AK124.837 CpG island
HCT116 Control AZA 48h TSA 24h AZA + TSA
#78 BC042064 CpG island
HCT116 Control AZA 48h TSA 24h AZA + TSA
#91 GPRIN1 CpG island
HCT116 Control AZA 48h TSA 24h AZA + TSA
Supplementary Figure 2.
DNA methylation data of methylated genes after 5azaC, TSA and co-treated cells. Individual CpG residues are represented by white circles (unmethylated), light gray circles (25% methylated), dark gray circles (50% methylated) and black circles (over 75% methylated). Data has been obtained by direct sequencing of sodium bisulfite treated DNAs. HRH2 CpG island recurrently failed to amplify the 5azaC treated DNAs. ND, not determined.
Resultats 158
A
SFXN1 THOC3
4.00E-01 4.00E-03
3.00E-01 3.00E-03
2.00E-01 2.00E-03
1.00E-01 1.00E-03 Absolute expression Absolute
0.00E+00 0.00E+00 HCT116 - AZA TSA AZA + TSA HCT116 AZA TSA AZA+TSA
B HRH2 CPLX2
1.00E-04 1.20E-03 1.00E-03 8.00E-05 8.00E-04 6.00E-05 6.00E-04 4.00E-05 4.00E-04
2.00E-05 2.00E-04 Absolute expression Absolute 0.00E+00 0.00E+00 HCT116 - AZA TSA AZA+TSA HCT116 AZA TSA AZA+TSA
C PcLKC CLTB
3.00E-04 7.00E-02 2.50E-04 6.00E-02
2.00E-04 5.00E-02 4.00E-02 1.50E-04 3.00E-02 1.00E-04 2.00E-02 5.00E-05
Absolute expressionAbsolute 1.00E-02 0.00E+00 0.00E+00 HCT116 AZA TSA AZA+TSA HCT116 - AZA TSA AZA + TSA
Supplementary Figure 3.
TSA treatment has different effects on the expression levels of genes across 5q35.2. Highly expressing genes become weakly downregulated after TSA treatment, similarly to the downregulation seen after 5azaC and drug co-treatment (A). DNA methylated, deeply silenced genes do not become upregulated after TSA treatment (B), while the downregulated PcLKC gene, which lacks promoter CpG Island, and the unmethylated and weakly downregulated CLTB gene, are the only genes in the region that weakly upregulate upon TSA treatment (C).